Below are the computer science course offerings for one semester. This list primarily includes courses that count without reservation towards CS program requirements, and MSSI program courses (650.xxx). Undergraduate majors might also want to consult the list of non-department courses that may be used as "CS other" in accordance with established credit restrictions.
- See the calendar layout for a day/time view of this course schedule.
- Click here for a printable version of this table only.
All undergraduate courses except EN.500.112 will initially be listed as CS/CE majors/minors only, plus some affiliated minors for certain courses. All graduate courses will initially be listed as CS & affiliated MSE programs only (differs by course). After the initial registration period for each group, these restrictions will be lifted. Current restriction expiration dates are the end of the 2nd week of registration for most undergraduate courses and August 9th for most graduate courses (after incoming graduate students have had a chance to register). Please be considerate of our faculty time and do not email them seeking permission to bypass these restrictions.
CS Course Area Designators - CS course area designators are used for various program requirememts and encoded as POS tags in SIS. There are 5 main areas and also 2 extra tags for undergraduates:
- CSCI-APPL Applications
- CSCI-RSNG Reasoning
- CSCI-SOFT Software
- CSCI-SYST Systems
- CSCI-THRY Theory
- CSCI-TEAM Team (undergraduate only)
- CSCI-ETHS Ethics (undergraduate only)
Course Numbering Note - Grad students must take courses 601.6xx and above to count towards their degrees. Combined bachelors/masters students may count courses numbered 601.4xx towards their masters degree if taken before the undergrad degree is completed. [All co-listed 601.4xx/6xx courses are equivalent.]
Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on SIS and the Registrar's website in September. Changes to the original SIS schedule will be noted in red.
|
500.112 (E) |
GATEWAY COMPUTING: JAVA (3) More This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of object-oriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. |
MWF 50 minutes, limit 19/section |
|
601.104 (H) |
COMPUTER ETHICS (1) Leschke
Students will examine a variety of topics regarding policy, legal, and
moral issues related to the computer science profession itself and to the
proliferation of computers in all aspects of society, especially in the era
of the Internet. The course will cover various general issues related to
ethical frameworks and apply those frameworks more specifically to the use
of computers and the Internet. The topics will include privacy issues,
computer crime, intellectual property law -- specifically copyright and
patent issues, globalization, and ethical responsibilities for computer
science professionals. Work in the course will consist of weekly
assignments on one or more of the readings and a final paper on a topic
chosen by the student and approved by the instructor.
|
Sec 01: Mon 4:30-6:00p |
|
601.124 (EH) |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Garg The expansion of artificial intelligence (AI)-enabled
use cases across a broad spectrum of domains has underscored the
benefits and risks of AI. This course will address the various ethical
considerations engineers need to engage with to build responsible and
trustworthy AI-enabled autonomous systems. Topics to be covered
include: values-based decision making, ethically aligned design,
cultural diversity, safety, bias, AI explainability, privacy, AI
regulation, the ethics of synthetic life, and the future of work. Case
studies will be utilized to illustrate real-world
applications. Students will apply learned material to a group research
project on a topic of their choice. |
MW 1:30-2:45p |
|
601.164 (EH) |
HUMAN & MACHINE INTELLIGENCE ALIGNMENT (3) Ryan
The challenge of ensuring that the actions of individuals and systems
— whether human or machine — are consistent with shared goals, reflect
our values, and promote societal well-being is known as "the alignment
problem." Over millennia, humans have developed many coordination and
cooperation "technologies" — such as customs, values, norms, laws,
organizations, governments, and markets—that partially solve the
problem of human intelligence alignment. |
Sec 01: TuTh 4:30-5:45p, limit 28, CS majors only |
|
601.220 (E)
|
INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover low-level programming techniques, as well as object-oriented class design, and the use of class libraries. Specific topics include pointers, dynamic memory allocation, polymorphism, overloading, inheritance, templates, collections, exceptions, and others as time permits. Students are expected to learn syntax and some language specific features independently. Course work involves significant programming projects in both languages. Prereq: AP CS or (>=C+ grade in one of 500.112, 500.113, 500.114, 580.200) or (500.132 or 500.133 or 500.134) or equivalent by permission. Students may not register until grades are posted. |
CS/CE/EE majors/minors only |
|
601.226 (EQ) |
DATA STRUCTURES (4) Madooei This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or (>= C+ grade in 601.220 or 500.112) or 500.132 or equivalent by permission. Students may not register until grades are posted. |
Sec 01: MWF 12-1:15pm, limit 75 |
|
601.229 (E) |
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer This course covers modern computer systems from a software perspective. Topics include binary data representation, machine arithmetic, assembly language, computer architecture, performance optimization, memory hierarchy and cache organization, virtual memory, Unix systems programming, network programming, and concurrency. Hardware and software interactions relevant to computer security are highlighted. Students will gain hands-on experience with these topics in a series of programming assignments. Prereq: 601.220. |
Sec 01: MWF 9-9:50am, limit 45 |
|
601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) Gagan This course provides an introduction to mathematical reasoning and discrete structures relevant to computer science. Topics include propositional and predicate logic, proof techniques including mathematical induction, sets, relations, functions, recurrences, counting techniques, simple computational models, asymptotic analysis, discrete probability, graphs, trees, and number theory. Pre-req: Gateway Computing (500.112/113/114/132/133/134 or AP CS or 601.220). Students can get credit for at most one of EN.601.230 or EN.601.231. |
Sec 01: TTh 12:00-1:15p, Th 4:30-5:20p |
|
601.257 (E) |
COMPUTER GRAPHICS & 3D GAME PROGRAMMING (3) Simari
In this course, students will program a game of their own design using
an off-the-shelf game engine while learning about the 3D computer
graphics concepts behind the engine's components. Classes will consist
of a mix of theory and practice. The theory will be presented through
lectures on topics including transformations, lighting, shading, shape
representations, spatial querying and indexing, animation, and special
effects. Practice will involve in-class programming exercises and
contributions to the game project with periodic in-class presentations
of progress to date. Students are expected to have a strong
programming background and to be familiar with basic linear algebra
concepts.
Prereq: 601.220, 601.226 and linear algebra. |
MWF 10a |
|
601.280 (E) |
FULL-STACK JAVASCRIPT (3) Presler-Marshall & Crainiceanu A full-stack JavaScript developer is a person who can build modern software applications using primarily the JavaScript programming language. Creating a modern software application involves integrating many technologies - from creating the user interface to saving information in a database and everything else in between and beyond. A full-stack developer is not an expert in everything. Rather, they are someone who is familiar with various (software application) frameworks and the ability to take a concept and turn it into a finished product. This course will teach you programming in JavaScript and introduce you to several JavaScript frameworks that would enable you to build modern web, cross-platform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a full-stack JavaScript developer. Prereq: 601.220 or 601.226. |
Sec 01 [Crainiceanu]: MW 12-1:15p, limit 39, CS majors/minors |
|
601.315 (E) |
DATABASES (3) Yarowsky Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
601.340 (E)
|
WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. Note: This undergrad version will not have the same paper component as the other versions of this course. Prerequisite: 601.226, 601.229 & 601.280. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.405 (E) |
NETWORK SECURITY Erik Rye This course focuses on
communication security in computer systems and networks. The course is
intended to provide students with an introduction to the field of
network security. The course covers network security services such as
authentication and access control, integrity and confidentiality of
data, firewalls and related technologies, Web security and
privacy. Course work involves implementing various security
techniques. A course project is
required. |
MWF 10 |
|
660.410 (E) |
CS INNOVATION AND ENTREPRENEURSHIP I (3) Dahbura & Aronhime
[Counts towards "CS other" credits and is a pre-requisite for 601.411
offered in the Spring.] |
MW 12-1:15 |
|
601.412 (E) |
BIG DATA SYSTEMS (3) Crainiceanu
This course introduces students to the field of big data
processing, its underlying technologies, managing the inter-relation
of components in a system at scale, and the construction of analytical
pipelines that provide business value. Key technologies explored in
this course will include NoSQL databases, Hadoop ecosystems,
distributed processing frameworks like Apache Spark, data warehousing
solutions such as Hive, and streaming platforms like Kafka. Emphasis
will be placed on understanding the architectural design and
operational challenges of big data ecosystems. Prior knowledge of
databases or parallel computing recommended but not required. |
TuTh 12-1:15p |
|
601.414 (E) |
COMPUTER NETWORKS (3) Marder Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
TuTh 3-4:15p |
|
601.415 (E) |
DATABASES (3) Yarowsky [Similar material as EN.601.315, covered in more depth for advanced undergraduates.] Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 601.226. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15pm |
|
601.420 (E) |
PARALLEL PROGRAMMING & PERFORMANCE ENGINEERING (3) Burns
This course guides the learner to write efficient software with focus
on exploiting hardware parallelism and efficient memory usage. Modern
microprocessors are remarkably complex and implement parallelism at
many levels, including instruction level, vectorization, pipelining,
multicore, and memory. Simple or naïve implementations realize only a
fraction of the performance available. Exploiting the capabilities or
processors require an understanding of algorithms, computer
architecture, systems (compilers, PL, OS) and how they interact. The
course programs mostly in C/C++, because most performance-oriented
software is written in these languages. It will also touch on at
parallel programming in Python and CUDA for GPUs. Familiarity with
Python required.
|
TuTh 4:30-5:45pm |
|
601.421 (E) |
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish This course covers object-oriented software construction
methodologies and their application. The main component of the course
is a large team project on a topic of your choosing. Course topics
covered include object-oriented analysis and design, UML, design
patterns, refactoring, program testing, code repositories, team
programming, and code reviews.
Prereq: 601.220, 601.226, and (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. |
MW 1:30-2:45p |
|
601.425 (E) |
SOFTWARE SYSTEM DESIGN (3) Madooei This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Prereq: EN.601.315/415/615 or EN.601.280 or EN.601.290 or EN.601.340/440/640 or EN.601.421/621), or permission. Students may receive credit for only one of 601.425/625. |
TuTh 12-1:15p |
|
601.428 (E) |
COMPILERS & INTERPRETERS (3) Hovemeyer
Introduction to compiler design, including lexical
analysis, parsing, syntax-directed translation, symbol tables,
run-time environments, and code generation and optimization. Students
are required to write a compiler as a course
project. Prereq: 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15pm |
|
601.429 (E) |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith How can we effectively use functional programming techniques to build real-world software? This course will primarily focus on using the OCaml programming language for this purpose. Topics covered include OCaml basics, modules, standard libraries, testing, quickcheck, build tools, functional data structures and efficiency analysis, monads, streams, and promises. Students will practice what they learn in lecture via functional programming assignments and a final project. Pre-req: 601.226 or instructor permission. Students can receive credit for only one of EN.601.429/EN.601.629. |
MW 1:30-2:45pm |
|
601.433 (EQ) |
INTRO ALGORITHMS (3) Dinitz & Sorrell This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as union-find); graph . algorithms and searching techniques such as minimum spanning trees, depth-first search, shortest paths, design of online algorithms and competitive analysis. Prereq: 601.226 & (553.171/172 or 601.230 or 601.231). Students may receive credit for only one of 601.433/633. |
Sec 01 (Dinitz): TuTh 1:30-2:45p, limit 40 |
|
601.438 (EQ) |
THEORY OF DIFFERENTIAL PRIVACY
(3) Lydia Zakynthinou
This course is an introduction to differential privacy as a
foundational framework for reasoning about privacy in data
analysis. Students will develop a principled understanding of why
privacy risks arise when privacy is not an explicit design objective
and how differential privacy enables formal, provable guarantees. In
this course, we will build on the algorithmic toolkit and statistical
techniques for designing and analyzing differentially private methods,
and study fundamental tradeoffs and lower bounds that characterize the
limits of privacy. Required course background: students should be
comfortable writing mathematical proofs involving algorithms,
probability, and linear algebra.
Prereq: 601.433/633 or permission. Students may receive credit for only one of 601.438/638. |
MW 4:30-5:45p |
|
601.439 (E) |
MACHINE LEARNING FOR SINGLE-CELL & SPATIAL GENOMICS (3) Uthsav Chitra Recent experimental advances
enable the measurement of DNA, RNA and other diverse molecular
modalities inside individual cells at an unprecedented scale and
resolution. Computational and machine learning (ML) methods are
essential for analyzing and interpreting these high-dimensional,
single-cell genomics datasets. This course introduces
computational/ML frameworks that are often used to analyze
modern single-cell and spatial datasets. Topics include but are
not limited to: matrix factorization; autoencoders and
contrastive learning; graphs and manifold learning; graph neural
networks; computational optimal transport (OT);
Gromov-Wasserstein and dynamic OT. Prerequisite: python programming and (EN.553.310 or EN.553.311 or EN.553.420 or EN.553.421) and (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) and (AS.110.202 OR AS.110.211). Students may receive credit for only one of EN.601.439 and EN.601.639. |
TuTh 4:30-5:45pm |
|
601.440 (E)
|
WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. Prerequisite: 601.226, 601.229 & 601.280. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15pm |
|
601.443 (E) |
SECURITY AND PRIVACY IN COMPUTING (3) Rushanan & Martin
Lecture topics will include computer security, network security, basic
cryptography, system design methodology, and privacy. There will be a
heavy work load, including written homework, programming assignments,
exams and a comprehensive final. The class will also include a
semester-long project that will be done in teams and will include a
presentation by each group to the class. Prerequisite: 601.229. Students may receive credit for only one of 601.443/643. |
MW 12-1:15p |
|
601.445 (E)
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverse-engineering undocumented cryptographic systems. Prereq: 601.226 & 601.229. Students may receive credit for only one of 601.445/645. |
MW 3-4:15p |
|
601.447 (E) |
COMPUTATIONAL GENOMICS: SEQUENCES (3) Langmead
Your genome is the blueprint for the molecules in your body. It's
also a string of letters (A, C, G and T) about 3 billion letters
long. How does this string give rise to you? Your heart, your brain,
your health? This, broadly speaking, is what genomics research is
about. This course will familiarize you with a breadth of topics from
the field of computational genomics. The emphasis is on current
research problems, real-world genomics data, and efficient software
implementations for analyzing data. Topics will include: string
matching, sequence alignment and indexing, assembly, and sequence
models. Course will involve significant programming projects.
Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747. |
TuTh 9-10:15am |
|
601.449 (E) |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.454 (E) CSCI-APPL |
INTRODUCTION TO AUGMENTED REALITY (3) Munawar
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Prerequisites: EN.601.220, EN.601.226, linear algebra. Students may receive credit for only one of 601.454/654. |
TuTh 3-4:15p |
|
601.455 (E) |
COMPUTER INTEGRATED SURGERY I (4) Taylor This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. Recommended Course Background: 601.220, 601.457, 601.461, image processing. (http://www.cisst.org/~cista/445/index.html) Prereq: 601.226 and linear algebra, or permission. Students may earn credit for only one of 601.455/655. |
TuTh 1:30-2:45pm |
601.457 (EQ) |
COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Prereq: no audits; 601.220 & 601.226 & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.460 (E) |
EMBODIED AI WITH WEB-SCALE VIDEO DATA (3) Homanga Bharadwaj
Embodied AI from Web-Scale Multimodal Data examines how modern agents
learn perception, prediction, and control by leveraging large,
unstructured internet data, especially web video, egocentric human
interaction recordings, and vision-language datasets. The course
builds a bottom-up understanding of the perception–action loop,
focusing on how motion, 3D structure, human pose and interaction
cues, and multimodal signals can be extracted and aligned from video
to support embodied reasoning and decision-making. Prereq: EN.601.475/675 or EN.601.482/682. Students may receive credit for only one of 601.460/660. |
TuTh 4:30-5:45p |
|
601.461 (EQ) |
COMPUTER VISION (3) Katyal & Hager This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine vision and biological vision are also included. Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
Sec 01 [Katyal]: Mon 4:30-7p, limit 40, CS/CE majors/minors |
|
601.463 (E) |
ALGORITHMS FOR SENSOR-BASED ROBOTICS (3) Leonard This course surveys the development of robotic systems for navigating in an environment from an algorithmic perspective. It will cover basic kinematics, configuration space concepts, motion planning, and localization and mapping. It will describe these concepts in the context of the ROS software system, and will present examples relevant to mobile platforms, manipulation, robotics surgery, and human-machine systems. Prereq: 601.226, Calc III, linear algebra & probability. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 12-1:15p, limit 15, CS/CE majors/minors |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque The course situates the study of Artificial Intelligence (AI) first in the broader context of Cognitive Science (i.e., human intelligence) and then treats in-depth principles and methods for reasoning, planning, and learning, including both conventional methods and recent deep learning approaches. The class is recommended for all scientists and engineers with a genuine curiosity about how to build an AI system (in particular, an intelligent agent) that can learn, reason about, and interact with the world and other agents. Strong programming skills and a solid mathematical foundation are expected. Students will be asked to complete both programming assignments and writing assignments. Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01: TuTh 1:30-2:45p, limit 50, CS/CE majors/minors |
|
601.465 (E) |
NATURAL LANGUAGE PROCESSING (4) Eisner An in-depth introduction to core techniques for analyzing, transforming, and generating human language. The course spans linguistics, modeling, algorithms, and applications. (1) How should linguistic structure and meaning be represented (e.g., trees, morphemes, λ-terms, vectors)? (2) How can we formally model the legal structures and their probabilities (e.g., grammars, automata, features, log-linear models, recurrent neural nets, Transformers)? (3) What algorithms can estimate the parameters of these models (e.g., gradient descent, EM) and efficiently identify probable structures (e.g., dynamic programming, beam search)? (4) Finally, what kinds of systems can be built with these techniques and how are they constructed and evaluated in practice? Detailed assignments guide students through many details of implementing core NLP methods. The course proceeds from first principles, although prior exposure to AI, statistics, ML, or linguistics can be helpful. (www.cs.jhu.edu/~jason/465) Prerequisite: 601.226 and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.467 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.468 (E) |
MACHINE TRANSLATION (3) Koehn
Google translate can instantly translate between any pair of over
fifty human languages (for instance, from French to English). How does
it do that? Why does it make the errors that it does? And how can you
build something better? Modern translation systems learn to translate
by reading millions of words of already translated text, and this
course will show you how they work. The course covers a diverse set of
fundamental building blocks from linguistics, machine learning,
algorithms, data structures, and formal language theory, along with
their application to a real and difficult problem in artificial
intelligence. Required course background: prob/stat, 601.226. Students may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.469 (E) |
AI SAFETY, ALIGNMENT & GOVERNANCE (3) Hadfield This course will focus on the alignment and governance challenges posed by advanced frontier/general purpose AI models: why these models may behave in ways that pose significant risk to human welfare and what technical and governance approaches might mitigate these risks. We’ll begin the course studying general results from alignment and governance in human normative systems such as markets, politics, norms and laws. We’ll pay special attention to risks arising from agentic AI. We’ll then look at current technical and position papers in various topics in AI safety and alignment. Topics could include: RLHF, constitutional AI, red-teaming, safety evaluation methods, red lines, jail-breaking, prompt injection, over-optimization, and open-source debates. We’ll conclude with discussion of regulatory frameworks such as regulatory markets, registration of frontier models, international governance organizations, registration of AI agents and legal personhood for AI agents. This is a paper-reading class. Prereq: A machine learning course: [601.474/674 OR 601.475/675 OR 601.482/682 OR 601.486/686] Students may receive credit for at most one of 601.469/669. |
TuTh 12-1:15p |
|
601.471 (E) |
NLP: SELF-SUPERVISED MODELS (3) Khashabi The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Pre-reqs: EN.601.226, Linear Algebra, and Probability, as well as familiarity with Python/PyTorch. |
TuTh 9-10:15a |
|
601.473 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Pre-reqs: Prob/Stat & Linear Algebra & Computing
[((EN.553.420 OR EN.553.421) AND (EN.553.430 OR
EN.553.431)) OR (EN.553.211 OR EN.553.310 OR EN.553.311) AND
(AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.500.112
OR EN.500.113 OR EN.500.114 OR EN.601.220 OR AS.250.205)]. |
TuTh 1:30-2:45p |
|
601.475 (E) |
MACHINE LEARNING (3) Oberst
Machine learning is subfield of computer science and artificial
intelligence, whose goal is to develop computational systems,
methods, and algorithms that can learn from data to improve their
performance. This course introduces the foundational concepts of
modern Machine Learning, including core principles, popular
algorithms and modeling platforms. This will include both supervised
learning, which includes popular algorithms like SVMs, logistic
regression, boosting and deep learning, as well as unsupervised
learning frameworks, which include Expectation Maximization and
graphical models. Homework assignments include a heavy programming
components, requiring students to implement several machine learning
algorithms in a common learning framework. Additionally, analytical
homework questions will explore various machine learning concepts,
building on the pre-requisites that include probability, linear
algebra, multi-variate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
Pre-reqs: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675. |
Sec 01: MWF 3-4:15p, limit 40, CS/CE majors/minors |
|
601.477 (EQ) |
CAUSAL INFERENCE (3) Shpitser "Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability (EN.601.475 OR (EN.553.310 OR EN.553.420)) AND AS.110.202). Students may receive credit for at most one of 601.477/677. |
MW 3-4:15p |
|
601.478 (E) |
CAUSAL DISCOVERY Kocaoglu Data often provides a projection of the inner workings of real-world systems. Many problems require a deeper understanding of the cause-and-effect relations that underlie the data-generating process. Causal discovery refers to the process of learning a graphical representation of these causal relations, called a causal graph. Such representations can be used for causal effect estimation, as well as other applications, such as system monitoring and diagnosis (e.g., root-cause analysis). In this course, we will learn the foundational principles behind causal discovery. We will explore the fundamental limits of causal discovery under well-defined assumptions and discuss algorithmic approaches for learning causal graphs from data. Pre-requisites: Machine Learning or Data Structures and Linear Algebra and Probability. Students may receive credit for at most one of 601.478/678. |
TuTh 3-4:15p |
|
601.479 (E) |
ML: REINFORCEMENT LEARNING (3) Braverman & Chiu
Tremendous success of reinforcement learning (RL) in a variety of
settings from AlphaGo to LLMs makes it a critical area to study. This
course will study classical aspects of RL as well as its modern
counterparts. Topics will include Markov Decision Processes, dynamic
programming, model-based and model-free RL, temporal difference
learning, Monte Carlo methods, multi-armed bandits, policy
optimization and other methods. Prereqs: [601.464/664 OR 601.475/675 OR 601.482/682] machine learning, [AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295] linear algebra and [EN.553.211 or EN.553.310 or EN.553.311 or EN.553.420 or EN.553.421] probability. Students may receive credit for at most one of 601.479/679. |
TuTh 12-1:15p |
|
601.482 (E) |
MACHINE LEARNING: DEEP LEARNING (4) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
data-intensive learning problems such as supervised learning for
classification or regression, dimensionality reduction, and control. As
such, it has a broad range of applications including speech and text
understanding, computer vision, medical imaging, and perception-based
robotics. Pre-req: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students can receive credit for EN.601.482 or EN.601.682, but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
601.483 (E) |
GENERATIVE VISION: THE ART & SCIENCE OF VISUAL SYNTHESIS Bhattad
This advanced course covers the evolution of generative modeling –
from early Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) to modern Autoregressive models and
Diffusion-based systems. Students will investigate how these
models represent the visual world, how they can be controlled,
and how they are transforming tasks like image editing, 3D scene
synthesis, and video generation. |
TuTh 3-4:15p |
|
601.485 (Q) |
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.685.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.
Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
EN.601.487 (E) |
ML: COPING WITH NON-STATIONARY ENVIRONMENTS AND ERRORS (3) Liu This course teaches machine learning methods that 1) consider data distribution shift and 2) represent and quantify the model uncertainty in a principled way. The topics we will cover include machine learning techniques that deal with data distribution shift, including domain adaptation, domain generalization, and distributionally robust learning techniques, and various uncertainty quantification methods, including Bayesian methods, conformal prediction methods, and model calibration methods. We will introduce these topics in the context of building trustworthy machine learning solutions to safety-critical applications and socially-responsible applications. For example, a typical application is responsible decision-making under uncertainty in non-stationary environments. So we will also introduce concepts like fair machine learning and learning under safety constraints, and discuss how robust and uncertainty-aware learning techniques contribute to such more desired systems. Students will learn the state-of-the-art methods in lectures, test their understanding in homeworks, and apply these methods in a project. Pre-req: 601.475/675. Students may receive credit for only one of 601.487/687, and may not take this course after taking EN.601.787. |
MW 3-4:15p |
|
601.489 (E) |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Pre-req: EN.601.475/675. |
MW 1:30-2:45 |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) Xiao & Reiter
This course is designed to introduce undergraduate and graduate
students to design techniques and practices in human-computer
interaction (HCI), the study of interactions between humans and
computing systems. Students will learn design techniques and
evaluation methods, as well as current practices and exploratory
approaches, in HCI through lectures, readings, and
assignments. Students will practice various design techniques and
evaluation methods through hands-on projects focusing on different
computing technologies and application domains. This course is
intended for undergraduate and graduate students in Computer
Science/Cognitive Science/Psychology. Interested students from
different disciplines should contact the instructor before enrolling
in this course. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Xiao): TuTh 3-4:15, limit 35, CS majors/minors |
|
601.493 (E) |
ACCESSIBLE COMPUTING (3) Huang This course is designed to introduce students to the principles, challenges, and opportunities in designing computing systems that are accessible to people with diverse abilities. Students will learn about assistive technologies, inclusive design methodologies, and the incorporation of accessibility in computer applications through lectures, readings, and projects. Pre-req: EN.601.290 or EN.601.490/690. Students may receive credit for EN.601.493 or EN.601.693, but not both. Interested students from non-CS disciplines should contact the instructor before enrolling in this course. |
MW 12-1:15p, limit 18, CS/CE majors/minors |
|
601.497 (E) NEW COURSE! |
HUMAN-CENTERED ROBOTICS: MODELS & ALGORITHMS (3) Haimin Hu
In this course, we will study fundamental concepts in human-centered
robotics, with an emphasis on mathematical models of human–robot
interaction and decision-making algorithms for safely deploying robots
in human-populated environments. We will ground these ideas in
applications such as drones, autonomous vehicles, and home
robots. After introducing the main technical tools for robot planning
and control in interactive, safety-critical settings through a
game-theoretic lens, we will turn our attention to two tightly coupled
challenges in modern robotics: safely enabling robots that learn from
humans and help humans learn. The course will combine seminar-style
discussions of research papers and whiteboard-style lecture to
introduce the key theoretical concepts, and the class project will
give you an opportunity to explore the approaches covered in class and
possibly combine them with your own research. After this class, you
will be familiar with the state of the art and open challenges in safe
and performant human–robot interaction, and you will understand the
guarantees and tradeoffs offered by different algorithmic frameworks
for human-centered robotics. Pre-req: EN.601.220, EN.601.226, Calc III, Linear Algebra, Probability. Students may receive credit for EN.601.497 or EN.601.697, but not both. |
MW 3-4:15p, limit 10 CS/CE majors/minors |
|
601.498 (E) |
HANDS-ON ROBOT LEARNING (3) Krishna Murthy Jatavallabhula
This course provides a comprehensive, hands-on introduction to the design and
implementation of modern learning-based robotic systems. The primary
objective of this course is to equip students with the theoretical skills and
practical tools necessary to succeed as world-class robot learning engineers and
scientists. Pre-req: (EN.601.475/675 or EN.601.482/682) and (EN.601.463/663 or EN.601.495/695 or EN.530.420). Students may receive credit for EN.601.498 or EN.601.698, but not both. |
Thu 3-5:30p |
|
601.501 |
COMPUTER SCIENCE WORKSHOP An applications-oriented, computer science project done under the supervision and with the sponsorship of a faculty member in the Department of Computer Science. Computer Science Workshop provides a student with an opportunity to apply theory and concepts of computer science to a significant project of mutual interest to the student and a Computer Science faculty member. Permission to enroll in CSW is granted by the faculty sponsor after his/her approval of a project proposal from the student. Interested students are advised to consult with Computer Science faculty members before preparing a Computer Science Workshop project proposal. Perm. of faculty supervisor req'd. |
See note at end regarding faculty section numbers |
|
601.503 |
INDEPENDENT STUDY Individual, guided study for undergraduate students under the direction of a faculty member in the department. The program of study, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See note at end regarding faculty section numbers |
|
601.507 |
UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See note at end regarding faculty section numbers |
|
601.509 |
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study and credit assigned must be worked out in advance between the student and the faculty member involved. Students may not receive credit for work that they are paid to do. As a rule of thumb, 40 hours of work is equivalent to one credit. S/U only. Permission required. |
See note at end regarding faculty section numbers |
|
601.513 |
GROUP UNDERGRADUATE PROJECT Independent learning and application for undergraduates under the direction of a faculty member in the department. This course has a regular project group meeting that students are expected to attend. The individual project contributions, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See note at end regarding faculty section numbers |
|
601.517 |
GROUP UNDERGRADUATE RESEARCH Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved. Permission required. |
See note at end regarding faculty section numbers |
|
601.519 |
SENIOR HONORS THESIS (3) For computer science majors only. The student will undertake a substantial independent research project under the supervision of a faculty member, potentially leading to the notation "Departmental Honors with Thesis" on the final transcript. Students are expected to enroll in both semesters of this course during their senior year. Project proposals must be submitted and accepted in the preceding spring semester (junior year) before registration. Students will present their work publically before April 1st of senior year. They will also submit a first draft of their project report (thesis document) at that time. Faculty will meet to decide if the thesis will be accepted for honors. Prereq: 3.5 GPA in Computer Science after spring of junior year and permission of faculty supervisor. |
See note at end regarding faculty section numbers |
|
601.556 |
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) Taylor The student will undertake a substantial independent research project in the area of computer-integrated surgery, under joint supervision of a WSE faculty adviser and a clinician or clinical researcher at the Johns Hopkins Medical School. Prereq: 601.455 or perm req'd. |
|
|
601.612 |
BIG DATA SYSTEMS (3) Crainiceanu
This course introduces students to the field of big data
processing, its underlying technologies, managing the inter-relation
of components in a system at scale, and the construction of analytical
pipelines that provide business value. Key technologies explored in
this course will include NoSQL databases, Hadoop ecosystems,
distributed processing frameworks like Apache Spark, data warehousing
solutions such as Hive, and streaming platforms like Kafka. Emphasis
will be placed on understanding the architectural design and
operational challenges of big data ecosystems. |
TuTh 12-1:15p |
|
601.614 |
COMPUTER NETWORKS (3) Marder Topics covered will include application layer protocols (e.g. HTTP, FTP, SMTP), transport layer protocols (UDP, TCP), network layer protocols (e.g. IP, ICMP), link layer protocols (e.g. Ethernet) and wireless protocols (e.g. IEEE 802.11). The course will also cover routing protocols such as link state and distance vector, multicast routing, and path vector protocols (e.g. BGP). The class will examine security issues such as firewalls and denial of service attacks. We will also study DNS, NAT, Web caching and CDNs, peer to peer, and protocol tunneling. Finally, we will explore security protocols (e.g. TLS, SSH, IPsec), as well as some basic cryptography necessary to understand these. Grading will be based on hands-on programming assignments, homeworks and two exams. Required Course Background: C/C++ programming and data structures, or permission. Students can only receive credit for one of 601.414/614. |
TuTh 3-4:15p |
|
601.615 |
DATABASES Yarowsky Introduction to database management systems and database design, focusing on the relational and object-oriented data models, query languages and query optimization, transaction processing, parallel and distributed databases, recovery and security issues, commercial systems and case studies, heterogeneous and multimedia databases, and data mining. (www.cs.jhu.edu/~yarowsky/cs415.html) Required course background: data structures. Students may receive credit for only one of 601.315/415/615. |
TuTh 3-4:15 |
|
601.620 |
PARALLEL PROGRAMMING & PERFORMANCE ENGINEERING (3) Burns
This course guides the learner to write efficient software with focus
on exploiting hardware parallelism and efficient memory usage. Modern
microprocessors are remarkably complex and implement parallelism at
many levels, including instruction level, vectorization, pipelining,
multicore, and memory. Simple or naïve implementations realize only a
fraction of the performance available. Exploiting the capabilities or
processors require an understanding of algorithms, computer
architecture, systems (compilers, PL, OS) and how they interact. The
course programs mostly in C/C++, because most performance-oriented
software is written in these languages. It will also touch on at
parallel programming in Python and CUDA for GPUs. Familiarity with
Python required.
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. Students can earn credit for only one of EN.601.420/EN.601.620. |
TuTh 4:30-5:45pm |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Darvish Same material as EN.601.421, for graduate students. This course covers object-oriented software construction methodologies and their application. The main component of the course is a large team project on a topic of your choosing. Course topics covered include object-oriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. Required course background: intermediate programming, data structures, and experience in mobile or web app development. Students may receive credit for only one of 601.421/621. |
MW 1:30-2:45p |
|
650.624 |
NETWORK SECURITY Erik Rye
This course focuses on communication security in computer systems and
networks. The course is intended to provide students with an
introduction to the field of network security. The course covers
network security services such as authentication and access control,
integrity and confidentiality of data, firewalls and related
technologies, Web security and privacy. Course work involves
implementing various security techniques. A course project is
required. |
MWF 10 |
|
601.625 |
SOFTWARE SYSTEM DESIGN Madooei This course introduces modern software systems design, with an emphasis on how to design large-scale systems, assess common system design trade-offs, and tackle system design challenges. It covers non-functional requirements, API design, distributed systems concepts, modern software building blocks (e.g., load balancers, caches, containers, etc.). Additionally, it includes case studies of common system design problems, some drawn from interview questions. Ultimately, this course helps learners become better software engineers. Required course background: (EN.601.315/415/615 or EN.601.280 or EN.601.290 or EN.601.340/440/640 or EN.601.421/621), or permission. Students may receive credit for only one of 601.425/625. |
TuTh 12-1:15p |
|
601.628 |
COMPILERS & INTERPRETERS Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntax-directed translation, symbol tables, run-time environments, and code generation and optimization. Students are required to write a compiler as a course project. Required Course Background: intermediate programming (C/C++), data structures, computer system fundamentals. Recommended: automata & computation theory. |
MW 12-1:15 |
|
601.629 |
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith
How can we effectively use functional programming techniques to build
real-world software? This course will primarily focus on using the
OCaml programming language for this purpose. Topics covered include
OCaml basics, modules, standard libraries, testing, quickcheck, build
tools, functional data structures and efficiency analysis, monads,
streams, and promises. Students will practice what they learn in
lecture via functional programming assignments and a final
project. |
MW 1:30-2:45pm |
|
601.633 |
INTRO ALGORITHMS Dinitz & Sorrell Same material as 601.433, for graduate students. Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633. |
Sec 01 [Dinitz]: TuTh 1:30-2:45p, limit 30, CS+MSSI |
|
601.638 |
THEORY OF DIFFERENTIAL PRIVACY
(3) Lydia Zakynthinou
This course is an introduction to differential privacy as a
foundational framework for reasoning about privacy in data
analysis. Students will develop a principled understanding of why
privacy risks arise when privacy is not an explicit design objective
and how differential privacy enables formal, provable guarantees. In
this course, we will build on the algorithmic toolkit and statistical
techniques for designing and analyzing differentially private methods,
and study fundamental tradeoffs and lower bounds that characterize the
limits of privacy. Required Course Background: students should be
comfortable writing mathematical proofs involving algorithms,
probability, and linear algebra.
Prereq: 601.433/633 or permission. Students may receive credit for only one of 601.438/638. |
MW 4:30-5:45p |
|
601.639 |
MACHINE LEARNING FOR SINGLE-CELL & SPATIAL GENOMICS (3) Uthsav Chitra Recent experimental advances enable the measurement of DNA, RNA and other diverse molecular modalities inside individual cells at an unprecedented scale and resolution. Computational and machine learning (ML) methods are essential for analyzing and interpreting these high-dimensional, single-cell genomics datasets. This course introduces computational/ML frameworks that are often used to analyze modern single-cell and spatial datasets. Topics include but are not limited to: matrix factorization; autoencoders and contrastive learning; graphs and manifold learning; graph neural networks; computational optimal transport (OT); Gromov-Wasserstein and dynamic OT. Required Course Background: python programming, probability, linear algebra, and multi-variable calculus. A machine learning/data science course is strongly recommended. No biology background is necessary. Students may receive credit for only one of EN.601.349 and EN.601.639. |
TuTh 4:30-5:45pm |
|
601.640 |
WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as same-origin policy, cross-origin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as cross-site scripting (XSS) and SQL injection, as well as how to attack and penetrate software with such vulnerabilities. Students will learn how to detect, respond, and recover from security incidents. Newly proposed research techniques will also be discussed. Required course background: data structures, computer system fundamentals and javascript/web development. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.643 |
SECURITY AND PRIVACY IN COMPUTING Rushanan & Martin Same material as 601.443, for graduate students. Lecture topics will include computer security, network security, basic cryptography, system design methodology, and privacy. There will be a heavy work load, including written homework, programming assignments, exams and a comprehensive final. The class will also include a semester-long project that will be done in teams and will include a presentation by each group to the class. Required course background: C programming and computer system fundamentals. |
MW 12-1:15p |
|
601.645
|
PRACTICAL CRYPTOGRAPHIC SYSTEMS Green Same material as 601.445, for graduate students. Prereqs: data structures & computer system fundamentals. Students may receive credit for only one of 601.445/645. |
MW 3-4:15p |
|
601.647 |
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Same material as 601.447, for graduate students. Required Course Background: Intermediate Programming (C/C++) and Data Structures. Students may earn credit for at most one of 601.447/647/747. |
TuTh 9-10:15 |
|
601.649 |
COMPUTATIONAL GENOMICS: APPLIED COMPARATIVE GENOMICS (3) Schatz [Formerly EN.601.749.] The goal of this course is to study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. Prereq: Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. |
MW 3-4:15p |
|
601.654 |
INTRODUCTION TO AUGMENTED REALITY (3) Munawar
This course introduces students to the field of Augmented Reality. It
reviews its basic definitions, principles, and applications. The
course explains how fundamentals concepts of computer vision are
applied for the development of Augmented Reality applications. It then
focuses on describing the principal components and particular
requirements to implement a solution using this technology. The course
also discusses the main issues of calibration, tracking, multi-modal
registration, advanced visualization, and display
technologies. Homework in this course will relate to the mathematical
methods used for calibration, tracking, and visualization in augmented
reality.
Required course background: intermediate programming (C/C++), data structures, linear algebra. Students may receive credit for only one of 601.454 or 601.654, but not both. |
TuTh 3-4:15p |
|
601.655 |
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. (http://www.cisst.org/~cista/445/index.html) This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of guest lectures given by surgeons and other experts on requirements and opportunities in particular clinical areas. Required Course Background: data structures, linear algebra, or permission. Recommended Course Background: intermediate programming in C/C++, computer graphics, computer vision, image processing, prob/stat. Students may earn credit for 601.455 or 601.655, but not both. |
TuTh 1:30-2:45 |
601.657 |
COMPUTER GRAPHICS Kazhdan Same material as 601.457, for graduate students. Prereq: no audits; Intermediate Programming (C/C++) & Data Structures & linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for only one of 601.457/657.
|
MWF 11 |
|
601.660 |
EMBODIED AI WITH WEB-SCALE VIDEO DATA (3) Homanga Bharadwaj
Embodied AI from Web-Scale Multimodal Data examines how modern agents
learn perception, prediction, and control by leveraging large,
unstructured internet data, especially web video, egocentric human
interaction recordings, and vision-language datasets. The course
builds a bottom-up understanding of the perception–action loop,
focusing on how motion, 3D structure, human pose and interaction
cues, and multimodal signals can be extracted and aligned from video
to support embodied reasoning and decision-making. |
TuTh 4:30-5:45p |
|
601.661 |
COMPUTER VISION Katyal & Hager Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
Sec 01 [Katyal]: Mon 4:30-7p, limit 30, CS+MSEM |
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. Required course background: data structures, Calc III, linear algebra & prob/stat. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 12-1:15p, limit 35, CS grads
Sec 02: TuTh 12-1:15p, limit 30, WSE + Non-ASEN grads |
|
601.664 |
ARTIFICIAL INTELLIGENCE Haque Same as 601.464, for graduate students. Required course background: data structures, linear algebra & prob/stat. Students can only receive credit for one of 601.464/664. |
Sec 01: TuTh 1:30-2:45p, limit 80, CS + MSEM grads |
|
601.665 |
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. (www.cs.jhu.edu/~jason/465) Prerequisite: data structures and basic familiarity with Python, partial derivatives, matrix multiplication, and probabilities. Students may receive credit for at most one of 601.465/665. |
Lect: MWF 3-4:15 |
|
601.667 (E) |
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. Pre-req: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. |
TuTh 9-10:15 |
|
601.668 |
MACHINE TRANSLATION Koehn Same material as 601.468, for graduate students. Required course background: prob/stat, data structures. Student may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.669 |
AI SAFETY, ALIGNMENT & GOVERNANCE (3) Hadfield This course will focus on the alignment and governance challenges posed by advanced frontier/general purpose AI models: why these models may behave in ways that pose significant risk to human welfare and what technical and governance approaches might mitigate these risks. We’ll begin the course studying general results from alignment and governance in human normative systems such as markets, politics, norms and laws. We’ll pay special attention to risks arising from agentic AI. We’ll then look at current technical and position papers in various topics in AI safety and alignment. Topics could include: RLHF, constitutional AI, red-teaming, safety evaluation methods, red lines, jail-breaking, prompt injection, over-optimization, and open-source debates. We’ll conclude with discussion of regulatory frameworks such as regulatory markets, registration of frontier models, international governance organizations, registration of AI agents and legal personhood for AI agents. This is a paper-reading class. Required Course Background: A machine learning course (equivalent to 601.474/674 OR 601.475/675 OR 601.482/682 OR 601.486/686) or permission. Students may receive credit for at most one of 601.469/669. |
TuTh 12-1:15 |
|
601.671 (E) |
NLP: SELF-SUPERVISED MODELS Khashabi The rise of massive self-supervised (pre-trained) models have transformed various data-driven fields such as natural language processing (NLP). In this course, students will gain a thorough introduction to self-supervised learning techniques for NLP applications. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own self-supervised neural network models, using the Pytorch framework. Students may receive credit for EN.601.471 or EN.601.671, but not both. Required course background: data structures, linear algebra, probability, familiarity with Python/PyTorch. |
TuTh 9-10:15a |
|
601.673 (E) |
COGNITIVE ARTIFICIAL INTELLIGENCE (3) Shu Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment.
Required Course Background: Prob/Stat & Linear Algebra &
Computing; prior course in ML/AI strongly recommended.
|
TuTh 1:30-2:45p |
|
601.675 |
MACHINE LEARNING Oberst
Same material as 601.475, for graduate students.
Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Student may receive credit for only one of 601.475/675. |
Sec 01: MWF 3-4:15p, limit 30, CS + MSEM grads |
|
601.677 |
CAUSAL INFERENCE Shpitser Same material as 601.477, for graduate students. Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for at most one of 601.477/677. |
MW 3-4:15p |
|
601.678 |
CAUSAL DISCOVERY Kocaoglu Data often provides a projection of the inner workings of real-world systems. Many problems require a deeper understanding of the cause-and-effect relations that underlie the data-generating process. Causal discovery refers to the process of learning a graphical representation of these causal relations, called a causal graph. Such representations can be used for causal effect estimation, as well as other applications, such as system monitoring and diagnosis (e.g., root-cause analysis). In this course, we will learn the foundational principles behind causal discovery. We will explore the fundamental limits of causal discovery under well-defined assumptions and discuss algorithmic approaches for learning causal graphs from data. Required Course Background: a graduate level course in machine learning or basics of data structures, linear algebra and probability. Students may receive credit for at most one of 601.478/678. |
TuTh 3-4:15p |
|
601.679 |
ML: REINFORCEMENT LEARNING (3) Braverman & Chiu
Tremendous success of reinforcement learning (RL) in a variety of
settings from AlphaGo to LLMs makes it a critical area to study. This
course will study classical aspects of RL as well as its modern
counterparts. Topics will include Markov Decision Processes, dynamic
programming, model-based and model-free RL, temporal difference
learning, Monte Carlo methods, multi-armed bandits, policy
optimization and other methods. Required course background: machine learning, linear algebra and probability. Students may receive credit for at most one of 601.479/679. |
TuTh 12-1:15p |
|
601.682 |
MACHINE LEARNING: DEEP LEARNING Unberath
Same as 601.482, for graduate students.
Required course background: Data Structures, Linear Algebra, Probability, Calc II required; Statistics, Machine Learning, Calc III, numerical optimization and Python strongly recommended. Students may receive credit for 601.482 or 601.682 but not both. |
MW 4:30-5:45p, F 4:30-5:20p |
|
601.683 |
GENERATIVE VISION: THE ART & SCIENCE OF VISUAL SYNTHESIS Bhattad
This advanced course covers the evolution of generative modeling –
from early Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) to modern Autoregressive models and
Diffusion-based systems. Students will investigate how these
models represent the visual world, how they can be controlled,
and how they are transforming tasks like image editing, 3D scene
synthesis, and video generation. |
TuTh 3-4:15p |
|
601.685 |
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Co-listed as AS.050.375/AS.050.675/EN.601.485.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. Pre-requisites: Calc I, programming experience (Python
preferred). Students can receive credit for at most one of
EN.601.485/EN.601.685/AS.050.375/AS.050.675.
|
TuTh 9-10:15 |
|
EN.601.687 |
ML: COPING WITH NON-STATIONARY ENVIRONMENTS AND ERRORS Liu This course teaches machine learning methods that 1) consider data distribution shift and 2) represent and quantify the model uncertainty in a principled way. The topics we will cover include machine learning techniques that deal with data distribution shift, including domain adaptation, domain generalization, and distributionally robust learning techniques, and various uncertainty quantification methods, including Bayesian methods, conformal prediction methods, and model calibration methods. We will introduce these topics in the context of building trustworthy machine learning solutions to safety-critical applications and socially-responsible applications. For example, a typical application is responsible decision-making under uncertainty in non-stationary environments. So we will also introduce concepts like fair machine learning and learning under safety constraints, and discuss how robust and uncertainty-aware learning techniques contribute to such more desired systems. Students will learn the state-of-the-art methods in lectures, test their understanding in homeworks, and apply these methods in a project. Required course background: 601.475/675 Machine Learning. Students may receive credit for only one of 601.487/687, and may not take this course after taking EN.601.787. |
MW 3-4:15p |
|
601.689 |
HUMAN-IN-THE-LOOP MACHINE LEARNING (3) Nalisnick
Machine learning (ML) is being deployed in increasingly consequential
tasks, such as healthcare and autonomous driving. For the foreseeable
future, successfully deploying ML in such settings will require close
collaboration and integration with humans, whether they be users,
designers, engineers, policy-makers, etc. This course will look at how
humans can be incorporated into the foundations of ML in a principled
way. The course will be broken down into three parts: demonstration,
collaboration, and oversight. Demonstration is about how machines can
learn from 'observing' humans---such as learning to drive a car from
data collected while humans drive. In this setting, the human is
assumed to be strictly better than the machine and so the primary goal
is to transmit the human's knowledge and abilities into the ML
model. The second part, collaboration, is about when humans and models
are near equals in performance but not in abilities. A relevant
setting is AI-assisted healthcare: perhaps a human radiologist and ML
model are good at diagnosing different kinds of diseases. Thus we will
look at methodologies that allow machines to ‘ask for help' when they
are either unconfident in their own performance and/or think the human
can better carry out the task. The course will close with the setting
in which machines are strictly better at a task than humans are, but
we still wish to monitor them to ensure safety and alignment with our
goals (oversight). Assessment will be done with homework, quizzes,
and a final project. Prerequisite: EN.601.475/675 or equivalent. |
MW 1:30-2:45 |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION Xiao & Reiter
Same material as EN.601.490, for graduate students. Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Xiao): TuTh 3-4:15, limit 18, CS only |
|
601.693 |
ACCESSIBLE COMPUTING (3) Huang This course is designed to introduce students to the principles, challenges, and opportunities in designing computing systems that are accessible to people with diverse abilities. Students will learn about assistive technologies, inclusive design methodologies, and the incorporation of accessibility in computer applications through lectures, readings, and projects. Required Background: programming and knowledge in human-computer interaction. Students may receive credit for EN.601.493 or EN.601.693, but not both. Interested students from non-CS disciplines should contact the instructor before enrolling in this course. |
MW 12-1:15p, limit 20, CS grad students |
|
601.697 NEW COURSE! |
HUMAN-CENTERED ROBOTICS: MODELS & ALGORITHMS (3) Haimin Hu In this course, we will study fundamental concepts in human-centered robotics, with an emphasis on mathematical models of human–robot interaction and decision-making algorithms for safely deploying robots in human-populated environments. We will ground these ideas in applications such as drones, autonomous vehicles, and home robots. After introducing the main technical tools for robot planning and control in interactive, safety-critical settings through a game-theoretic lens, we will turn our attention to two tightly coupled challenges in modern robotics: safely enabling robots that learn from humans and help humans learn. The course will combine seminar-style discussions of research papers and whiteboard-style lecture to introduce the key theoretical concepts, and the class project will give you an opportunity to explore the approaches covered in class and possibly combine them with your own research. After this class, you will be familiar with the state of the art and open challenges in safe and performant human–robot interaction, and you will understand the guarantees and tradeoffs offered by different algorithmic frameworks for human-centered robotics. Required student background: C/C++, data structures, calc III, linear algebra, probability. Students may receive credit for EN.601.497 or EN.601.697, but not both. |
Sec 01: MW 3-4:15p, limit 10 CS+MSEM grads |
|
601.698 |
HANDS-ON ROBOT LEARNING (3) Krishna Murthy Jatavallabhula
This course provides a comprehensive, hands-on introduction to the design and
implementation of modern learning-based robotic systems. The primary
objective of this course is to equip students with the theoretical skills and
practical tools necessary to succeed as world-class robot learning engineers and
scientists. Required Course Background: machine learning or deep learning, and robotics. Students may receive credit for EN.601.498 or EN.601.698, but not both. |
Thu 3-5:30p |
|
601.713 |
FUTURE NETWORKS Sabnani
Early networks were used for short message exchanges (Telegraph), and
then the world moved to voice telephony. Today, the Internet’s
dominant traffic is entertainment video. More and more objects (IoT
devices) are connected to the Internet for control and
monitoring. With the need for enormous AI computations, new networks
with gigantic capacity are being designed and built. These
transformations require transferring large amounts of information,
rapidly deploying new features, and simpler management.
The course will start with a brief introduction to the past networks:
telegraph and telephone networks. Then, it will move to today's
Internet. Endpoints are not just humans but also objects and machines;
the Internet is increasingly becoming a network of objects.
The course will mostly focus on how these networks will evolve in the
future. New applications such as autonomous driving require networking
and computing to be embedded together. This feature is already
beginning to be implemented in 5G and 6G networks; 6G will also allow
networks to be used as sensors. New technologies such as mobile edge
computing, software-defined networking (SDN), network slicing, digital
twins, and named-data networking (NDN) enable these advances. Two
timely topics – Web 3 and the application of machine learning to
networking – have been added. V2X networks will be a strong focus.
Students will be required to participate in discussions on this
topic. Students will be asked to study new papers and do course
projects, which should result in longer-term research projects.
|
Tu 4:30-7p |
|
601.714 |
ADVANCED COMPUTER NETWORKS Ghorbani
This is a graduate-level course on computer networks. It provides
a comprehensive overview on advanced topics in network protocols
and networked systems. The course will cover both classic papers
on Internet protocols and recent research results. It will examine
a wide range of topics, e.g., routing, congestion control, network
architectures, datacenter networks, network virtualization,
software-defined networking, and programmable networks, with an
emphasize on core networking concepts and principles. The course
will include lectures, paper discussions, programming assignments
and a research project. |
TuTh 3-4:15p |
|
601.716 |
ADVANCED TOPICS IN INTERNET OF THINGS Zhao This course explores the convergence of computer networks, mobile computing, and embedded systems, with a specific focus on the Internet of Things (IoT). IoT represents a paradigm shift in computing, aiming to bridge the gap between the physical and digital worlds. Its development has opened up new possibilities, including mobile health, smart homes, industrial automation, and more. Throughout the course, students will delve into advanced topics such as IoT networking, mobile and edge computing, embedded machine learning, wireless sensing, human-computer interaction, and mobile health applications. To excel in this course, students are expected to engage in pre-class readings and in-class discussions, and complete a semester-long project. The focus of the course will be on training research philosophy and principles instead of papers' technical details. The course covers multiple disciplines and encourages interdisciplinary projects; students with diverse backgrounds such as computer science/engineering, electrical engineering, biomedical engineering or other related areas are welcomed. Recommended Course Background: familiarity with computer system fundamentals, computer networks, signal processing, and mobile computing. |
TuTh 1:30-2:45p |
|
601.727 |
MACHINE PROGRAMMING Ziyang Li
Programs are the fundamental medium through which humans interact with
computers. With the advent of large language models (LLMs), the
automated synthesis of programs is rapidly transforming how we build
software. Instead of manual code writing, we specify intent through
examples, specifications, and natural language. Required course background: Python proficiency and LLM familiarity. |
TuTh 12-1:15p |
|
601.734 |
TOPICS IN MACHINE LEARNING-AUGMENTED ALGORITHM DESIGN Nicolas Christianson Artificial intelligence and machine learning hold significant promise for improving algorithmic decision-making across domains. This course will survey recent advances in integrating AI/ML models into algorithm design. We will focus on two different paradigms: algorithms with predictions, which seek to leverage black-box, potentially unreliable predictions to improve performance while maintaining robustness; and learning-based approaches, where AI/ML models are trained to directly perform algorithmic reasoning. Throughout, we will emphasize settings where provable guarantees – such as robustness to prediction error and generalization bounds – can be obtained. Required course background: 601.433/633 Algorithms and 601.475/675 Machine Learning or equivalent. |
TuTh 9-10:15a |
|
601.762 |
MULTIMODAL UNDERSTANDING AND GENERATION Jaemin Cho
This course provides a deep dive into modern Multimodal AI, focusing
on models that integrate vision and language data. Topics include
visual question answering, generative media (images/video),
neuro-symbolic AI, and embodied AI agents. Through weekly paper
reviews and a hands-on independent research project, students will
gain the technical skills to understand and advance the
state-of-the-art in multimodal deep learning. |
MW 1:30-2:45p |
|
601.768 |
LANGUAGE MODEL AGENTS (3) VanDurme Recent advancement in language models have given rise to a new generation of autonomous reasoning agents capable of executing valuable and challenging tasks. This advanced graduate course covers the emerging space of agentic systems with an emphasis on “safely confident reasoning". We will jointly study the design of agents and the development of the foundational models that power these systems. We will discuss topics integral to real-world agent deployments, such as efficiency and security concerns, uncertainty quantification and explainability for accountable decision making, indexing and retrieval tools that power agent memory. We conclude by discussing future directions for this emerging technology. Required Course Background: Machine Learning, ML: Deep Learning, ML: Learning Theory, ML: Reinforcement Learning, ML: Artificial System Design and Development, NLP: Self-Supervised Models, or equivalent (grad course in machine learning). |
TuTh 9-10:15a |
|
601.770 |
AI ETHICS AND SOCIAL IMPACT Field
AI is poised to have an enormous impact on society. What should that
impact be and who should get to decide it? The goal of this course
is to critically examine AI research and deployment pipelines,
with in-depth examinations of how we need to understand social
structures to understand impact. In application domains, we will
examine questions like “who are key stakeholders?”, “who is
affected by this technology?” and “who benefits from this
technology?”. We will also conversely examine: how can AI help us
learn about these domains, and can we build from this knowledge to
design AI for "social good"? As a graduate-level course, topics
will focus on current research including development and
deployment of technologies like large language models and decision
support tools, and students will conduct a final research project.
|
Mon 9-11:30a |
|
601.772 |
ADV TOPICS IN AI FOR SOCIAL SCIENCE DISCOVERY Gligoric
This advanced course explores state-of-the-art AI methods for social
science research, focusing on LLM-based approaches including
synthetic participants, AI-augmented surveys, and computational
simulations of human behavior, including historical
participants. Students will critically evaluate recent work
|
MW 4:30-5:45p |
|
601.779 |
MACHINE LEARNING: ADVANCED TOPICS (3) Arora
[Formerly called Advanced topics in Representation Learning] Pre-requisites: Representation Learning or
permission (requiring all of the following):
|
MW 12-1:15 |
|
601.794 |
PRIVACY TECHNOLOGY, DESIGN, AND LAW Yaxing Yao
Privacy has long been considered as a fundamental human
right. Emerging technologies such as social media, smart grid,
Internet of Things, drones, and self-driving cars have raised
heightened privacy issues. Recent developments of regulations such as
the European Union's General Data Protection Regulation (GDPR) and
California Consumer Privacy Act (CCPA) is also drawing increasing
attention from technologists, policymakers, and the media. How to
protect people's privacy is a key challenge of our time. Recommended course background: EN.601.443/643 Security & Privacy or equivalent. |
MW 12-1:15p |
|
601.796 |
SOCIAL COMPUTING Piccardi
This course is designed for graduate students, particularly PhD
students, to provide a comprehensive introduction to social computing
research. The course covers how social computing systems shape human
interaction, information exchange, collaboration, and collective
behavior in online environments. Core topics include social influence
and network effects, collaborative and peer production systems,
algorithmic curation, content amplification, and online social
dynamics. Additional topics include governance and content moderation,
online information integrity, auditing of sociotechnical systems,
antisocial behavior online, and social AI systems. |
TuTh 4:30-5:45p |
|
601.801 |
COMPUTER SCIENCE SEMINAR Attendance recommended for all grad students; only 1st & 2nd year PhD students may register. |
TuTh 10:30-12 |
|
601.803 |
MASTERS RESEARCH Independent research for masters students. Permission required. |
See note at end regarding faculty section
numbers. |
|
601.805 |
GRADUATE INDEPENDENT STUDY Permission required. |
See note at end regarding faculty
section numbers. |
|
601.806 |
CS GRADUATE PRACTICUM Smith Computer science graduate students engaging in external internships to gain practical experience in the field should enroll in this course. Permission required. |
limit 30, P/F only |
|
601.807 |
TEACHING PRACTICUM Selinski, Smith PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required. |
limit 30 |
|
601.809 |
PHD RESEARCH Independent research for PhD students. |
See note at end regarding faculty section
numbers. |
|
AS.050.814 |
RESEARCH SEMINAR IN COMPUTER VISION Yuille This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing. |
tba |
|
601.826 |
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This course covers recent developments in the foundations of programming language design and implementation. topics covered vary from year to year. Students will present papers orally. |
Fri 11 |
|
601.831 |
CS THEORY SEMINAR Dinitz, Li Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers. |
W 12 |
|
601.857 | SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. |
W 3p |
|
601.864 |
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING Yarowsky/Murray This is a weekly reading group focused on Natural Language Processing (NLP) outside of English. Whereas methods have gotten very strong in recent years on English NLP tasks, many methods fail on other languages due to both linguistic differences as well as lack of available annotated resources. This course will focus on Cross-Language Information Retrieval, Cross-Lingual Information Extraction, Multilingual Semantics, Massively Multilingual Language Modeling, and other non-English NLP sub-fields. Students will be expected to read, discuss, and present papers. Required course background: EN.601.465/665. |
Th 12-1p |
|
601.865 |
SELECTED TOPICS IN NATURAL LANGUAGE PROCESSING Eisner
A reading group exploring important current research in the field
and potentially relevant material from related fields. In addition to
reading and discussing each week's paper, enrolled
students are expected to take turns selecting papers and leading the discussion. |
W 12-1:15p |
|
601.868 |
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. |
M 11-noon |
|
520.807 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff CLSP seminar series, for any students interested in current topics in language and speech processing. |
M & F 12-1:15 |
|
500.745 |
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Whitcomb, Vidal, Etienne-Cummings Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided intervention. Sensor based robotics, including computer vision and biomedical image analysis. Algorithmic robotics, robot control and machine learning. Autonomous robotics for monitoring, exploration and manipulation with applications in home, environmental (land, sea, space), and defense areas. Biorobotics and neuromechanics, including devices, algorithms and approaches to robotics inspired by principles in biomechanics and neuroscience. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. |
Wed 12-1:30 |
|
650.601 |
INTRODUCTION TO INFORMATION SECURITY Xiangyang Li This course exposes students to the cross-disciplinary and broad information security field. It surveys a range of fundamental topics of information security principles, architecture, policy and standard, risk management, cryptography, physical, operation, system and network security mechanisms, and law and ethics, among others. This course includes lectures, case studies, and homework. Students will also complete independent study class projects. Recommended Course Background: Basic knowledge of computer system and information technology. |
TuTh 12-1:15 |
|
650.614 |
RIGHTS IN THE DIGITAL AGE Michael Jacobs This course will examine various legal and policy issues presented by the tremendous growth in computer technology, especially the Internet. The rights that various parties have with respect to creating, modifying, using, distributing, storing, and copying digital data will be explored. The concurrent responsibilities, and potential liabilities, of those parties will also be addressed. The course will focus on intellectual property issues, especially copyright law, and other legal and economic considerations related to the use and management of digital data. Copyright law and its role within the framework of intellectual property law will be presented in a historical context with an emphasis on its applicability to emerging-technology issues. Specifically, the treatment of various works, such as music, film, and photography that were traditionally, analog in nature will be analyzed with respect to their treatment in the digital domain; works that are by their nature digital, such as computer software, will also be analyzed. The current state of U.S. copyright law will be presented, as will relevant international treaties and foreign laws. The goal of the course is to provide those involved or interested in digital rights management with a general awareness of the rights and obligations associated with maintaining and distributing digital data. (This course will be taught in Washington, DC and video-cast on Homewood Campus.) |
W 4:30-6:30p |
|
650.621 |
CRITICAL INFRASTRUCTURE PROTECTION Lanier Watkins This course focuses on understanding the history, the vulnerability, and the need to protect our Critical Infrastructure and Key Resources (CIKR). We will start by briefly surveying the policies which define the issues surrounding CIKR and the strategies that have been identified to protect them. Most importantly, we will take a comprehensive approach to evaluating the technical vulnerabilities of the 18 identified sectors, and we will discuss the tactics that are necessary to mitigate the risks associated with each sector. These vulnerabilities will be discussed from the perspective of ACM, IEEE or other technical journals/articles which detail recent and relevant network-level CIKR exploits. We will cover well known vulnerable systems such the Internet, SCADA or PLC and lesser known systems such as E911 and industrial robot. Also, a class project is required. Recommended Course Background: EN.650.424 or equivalent or permission by instructor. |
Th 4:30-7p |
|
650.656 |
COMPUTER FORENSICS Timothy Leschke This course introduces students to the field of computer forensics and it will focus on the various contemporary policy issues and applied technologies. Topics to be covered include: legal and regulatory issues, investigation techniques, data analysis approaches, and incident response procedures for Windows and UNIX systems. Homework in this course will relate to laboratory assignments and research exercises. Students should also expect that a group project will be integrated into this course. |
W 6:30-9:00p |
|
650.658 |
INTRODUCTION TO CRYPTOGRAPHY Xiangyang Li Cryptography has a rich history as one of the foundations of information security. This course serves as the introduction to the working primitives, development and various techniques in this field. It emphasizes reasoning about the constraint and construction of cryptographic protocols that use shared secret key or public key. Students will also be exposed to some current open problems. Permission of instructor only. |
MW 12-1:15p |
|
650.660 |
SOFTWARE VULNERABILITY ANALYSIS Reuben Johnston Competent execution of security assessments on modern software systems requires extensive knowledge in numerous technical domains and comprehensive understanding of security risks. This course provides necessary background knowledge and examines relevant theories for software vulnerabilities and exploits in detail. Key topics include historical vulnerabilities, their corresponding exploits, and associated risk mitigations. Fundamental tools and techniques for performing security assessments (e.g., software reverse engineering, static analysis, and dynamic analysis) are covered extensively. The format of this course includes lectures and assignments where students learn how to develop exploits to well-known historical vulnerabilities in a controlled environment. Students will complete and demonstrate a project as part of the course. |
Fri 4:15-6:45p |
|
650.673 |
MOBILE AND WIRELESS SECURITY Ashutosh Dutta The past few decades have seen a rapid evolution of wireless LAN and cellular technologies. In addition to wireless access technologies, various types of network layer and application layer mobility protocols have been developed to provide seamless connectivity to mobile users. Maintaining end-to-end security for these mobile users needs to take into account authentication, authorization, integrity and confidentiality as mobile devices change their point-of-attachment. This course will provide an overview of various wireless access technologies, mobility protocol taxonomy and will describe end-to-end security including mobile end point, radio access network, network core, and application services. In addition, this will include hands-on lab experiments to examine security over wireless and mobile networks and a research group project. Overall objective of this course is to impart both theoretical and practical knowledge to the students, and at the same time make them ready for any future research to solve complex problems. Recommended Course Background: Knowledge of TCP/IP, Linux, Fundamentals of Networking. |
Tu 4:30-7p |
|
650.683 |
CYBERSECURITY RISK MANAGEMENT Javad Abed Data breaches, cyber attacks, cybercrime,
and information operations in social media continue to increase
in frequency and severity, causing businesses and governments to
focus more resources on cybersecurity risk management and
compliance. Utilizing real-world data breaches and attacks as
motivation, this course will provide students knowledge of risk
management concepts, frameworks, compliance regimes and best
industry practices used to ensure sound cybersecurity practices
in government, commercial, and academic organizations. Lab
exercises will provide opportunities for students to experience
key aspects of the risk management process and help prepare them
for post-graduation assignments as cybersecurity professionals.
|
F 1:30-4p |
|
650.836 |
INFORMATION SECURITY PROJECTS Dahbura, Li All MSSI programs must include a project involving a research and development oriented investigation focused on an approved topic addressing the field of information security and assurance from the perspective of relevant applications and/or theory. There must be project supervision and approval involving a JHUISI affiliated faculty member. A project can be conducted individually or within a team-structured environment comprised of MSSI students and an advisor. A successful project must result in an associated report suitable for on-line distribution. When appropriate, a project can also lead to the development of a so-called "deliverable" such as software or a prototype system. Projects can be sponsored by government/industry partners and affiliates of the Information Security Institute, and can also be related to faculty research programs supported by grants and Contracts. Required course for any full-time MSSI student. Open to MSSI students. Permission required for non-MSSI students. |
MW 11-11:50a |
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650.840 |
INFORMATION SECURITY INDEPENDENT STUDY Xiangyang Li Individual study in an area of mutual interest to a graduate student and a faculty member in the Institute. |
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Faculty section numbers for all independent type courses, undergraduate and graduate may vary by course. Please first check SIS to see if there is an active section for the needed course/faculty member. If not, the faculty member should email courses@cs to request one for the specific course needed. (CS primary tenure-track faculty will all have open sections for EN.601.809 PhD Research, but joint faculty might not until active.)