Below are the computer science course offerings for one semester. This list only includes courses that count without reservation towards CS program requirements. 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 convenient listing of course times.
- 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 4/22/2022 for most undergraduate courses and 7/25/2022 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.
New Area Designators - CS course area designators were changed effective July 2019. Previously there were 3 designations - Analysis, Systems, Applications - and these still appear in the course descriptions below for grandfathering purposes. Currently there are 5 areas and many courses have been reclassified. These areas will be implemented as POS (program of study) tags in SIS and are listed below each course number in the listings table. There are also 2 extra tags for undergraduates. Here are the new areas and tags:
- 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 - In order to be compliant with undergraduate students only in courses <=5xx and graduate students in courses >=6xx, we completely renumbered all the courses in the department in Fall 2017, with a 601 prefix instead of the old 600 prefix. (Courses are listed here with new numbers only.) 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.]
Teaching Modes - The university is anticipating a return to fully in-person course delivery for Fall 2022.
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.
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501.123 |
FYS: EXLPORING COMPUTER SCIENCE (1) staff This course provides first-year students with an introduction to the field and department. Faculty will lead weekly small group discussion sections on topics of interest related to the discipline. Upper-year CS majors will serve as peer mentors for each group. Satisfactory/Unsatisfactory only. |
Sec 01: Burns, Wed 6-6:50p |
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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 |
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601.124 (EH) NEW COURSE! |
THE ETHICS OF ARTIFICIAL INTELLIGENCE & AUTOMATION (3) Lopez-Gonzalez 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. |
Sec 01: MW 12-1:15p |
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601.220 (E)
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INTERMEDIATE PROGRAMMING (4) Darvish/Simari 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 601.107, 500.112, 500.113, 500.114, 580.200) or (500.132 or 500.133 or 500.134) or equivalent by permission. |
CS/CE/EE majors/minors only |
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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 601.107, 601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission. |
Sec 01: MWF 12-1:15pm, limit |
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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 47 |
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601.230 (EQ) |
MATHEMATICAL FOUNDATIONS FOR COMPUTER SCIENCE (4) More 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: MWF 9-9:50, W 4:30-5:20 |
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601.231 (EQ) |
AUTOMATA and COMPUTATION THEORY (3) More This course is an introduction to the theory of computing. Topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization. Students may not receive credit for 601.231 and 601.631 for the same degree. Prereq: 550/553.171/172. |
Lectures: M 12-1:15p |
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601.270 (E) |
OPEN SOURCE SOFTWARE ENGINEERING (Semesters of Code I) (3) Walli The course will provide students a development experience focused on learning software engineering skills to deliver software at scale to a broad community of users associated with open source licensed projects. The class work will introduce students to ideas behind open source software with structured modules on recognizing and building healthy project structure, intellectual property basics, community & project governance, social and ethical concerns, and software economics. The practical side of the course will engage and mentor students directly in OSI-licensed project communities to provide hands-on learning experiences of practices covered in the classroom modules, and team building experience working in the project. Prereq: 601.220 and 601.226. |
TuTh 10:30-11:45am |
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601.280 (E) |
FULL-STACK JAVASCRIPT (3) Madooei 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. |
TuTh 12-1:15pm |
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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. [Systems] (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 |
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601.318 (E) |
OPERATING SYSTEMS (3) Huang This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming and processing, device drivers, operating system components (e.g., file system, kernel), modeling and performance measurement, protection and security, and recent innovations in operating system structure. Course work includes the implementation of operating systems techniques and routines, and critical parts of a small but functional operating system. [Systems] Prereq: 601.220 & 601.226 & 601.229. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45pm |
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601.340 (E)
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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. [Systems] 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 |
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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 3-4:15 |
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601.414 (E) |
COMPUTER NETWORKS (3) Ghorbani 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. [Systems] Prerequisites: EN.601.226 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. |
MW 3-4:15 |
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601.415 (E) |
DATABASES (3) Yarowsky Similar material as 601.315, covered in more depth, for advanced undergraduates. [Systems] (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 |
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601.418 (E) |
OPERATING SYSTEMS (3) Huang Similar material as 601.318, covered in more depth, for advanced undergraduates. [Systems] Prereq: 601.220 & 601.226 & 601.229. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45pm |
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601.420 (E) NEW COURSE DESIGN |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience. [Systems]
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
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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. [(Systems or Applications), Oral] (https://www.jhu-oose.com) Prereq: 600/601.226, 600.120/601.220, and (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621. |
MWF 4:30-5:20p |
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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. [Systems]> Prereq: 601.220, 601.226 & 601.229 required; 601.230 or 601.231 recommended |
MW 12-1:15pm |
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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. |
MW 1:30-2:45pm |
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601.433 (EQ) |
INTRO ALGORITHMS (3) Garg 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. [Analysis] Prereq: 601.226 & (550/553.171/172 or 601.230 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633. |
TuTh 1:30-2:45pm |
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601.437 (EQ) |
FEDERATED LEARNING & ANALYTICS (3) Braverman Federated Learning (FL) is an area of machine learning where data is distributed across multiple devices and training is performed without exchanging the data between devices. FL can be contrasted with classical machine learning settings when data is available in a central location. As such, FL faces additional challenges and limitations such as privacy and communication. For example, FL may deal with questions of learning from sensitive data on mobile devices while protecting privacy of individual users and dealing with low power and limited communication. As a result, FL requires knowledge of many interdisciplinary areas such as differential privacy, distributed optimization, sketching algorithms, compression and more. In this course students will learn basic concepts and algorithms for FL and federated analytics, and gain hands-on experience with new methods and techniques. Students will gain understanding in reasoning about possible trade-offs between privacy, accuracy and communication. [Analysis] Prereq: 601.433/633, prob/stat, and (601.464/664 or 601.475/675). Students may receive credit for only one of 601.437/637. |
TuTh 12-1:15pm |
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601.440 (E)
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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. [Systems] Prerequisite: 601.226, 601.229 & 601.280. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15pm |
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601.442 (EQ) |
MODERN CRYPTOGRAPHY (3) Jain Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis] Prerequisite: (601.231/601.631 or 601.230) & (550.310/553.310 or 553.331 or 550.420/553.420). Students may receive credit for only one of 601.442/642. |
MW 1:30-2:45pm |
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601.443 (E) |
SECURITY AND PRIVACY IN COMPUTING (3) Jois 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. [Applications]
Prerequisite: 601.229 |
Lecture: Tu 1:30-2:40p |
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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. [Applications, Oral] Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747. |
TuTh 9-10:15am |
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601.449 |
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. [Applications] Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. |
MW 1:30-2:45 |
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601.451 (E) NEW COURSE! |
INTRODUCTION TO COMPUTATIONAL IMMUNOGENOMICS (3) Safonova Immunology studies defensive mechanisms of living organisms against external threats. Computational immunogenomics is a new field of bioinformatics that develops and applies computational approaches to the study and interpretation of immunological data, seeking to answer questions about adaptive immune responses in humans and important animals. In this course, students will learn how to design, apply, and benchmark algorithms for solving immunogenomics problems. [Applications] Students may receive credit for only one of EN.601.451, EN.601.651. Prereq: EN.601.220 and EN.601.226. |
TuTh 12-1:15p |
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601.454 (E) CSCI-APPL ADDED! |
AUGMENTED REALITY (3) Martin-Gomez
Same as 601.654, for undergraduate students.
[Applications] Students may receive credit for only one of 601.454/654. Prerequisites: EN.601.220, EN.601.226, linear algebra. |
TuTh 3-4:15p |
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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. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: 601.226 and linear algebra, or permission. Recmd: 601.220, 601.457, 601.461, image processing. 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. [Applications] 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.
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MWF 11 |
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601.461 (EQ) |
COMPUTER VISION (3) Katyal 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. [Applications] Prereq: intro programming, linear algebra, prob/stat. Students can earn credit for at most one of 601.461/661/761. |
TuTh 12-1:15 |
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601.462 (E) CANCELED! |
INTRODUCTION TO SPATIAL COMPUTING (3) Azimi This course will provide students with a rich understanding of immersive technology and spatial computing, including virtual, augmented, and mixed reality as the next wave of computing after personal and mobile computing, and belongs to the systems that can sense the space or are “spatially” aware. It also covers input systems and interaction modalities that have evolved to support human-computer interaction required for immersive environments. It will go through principles of design thinking including verification and validation, as a mindset for creating immersive experiences, and students will explore the practical implication of this subject in healthcare, industry, and society through the projects. [Applications] Prereq: 601.220, 601.226 and linear algebra required; 601.461/661 recommended. Students can earn credit for at most one of 601.462/662. |
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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. [Analysis] Prereq: 601.226 & linear algebra & probability. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15 |
601.464 (E) |
ARTIFICIAL INTELLIGENCE (3) Haque The class is recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as learning, planning and prediction. Materials will be primarily based on the popular textbook, Artificial Intelligence: A Modern Approach. Strong programming skills are expected, as well as basic familiarity with probability. For students intending to also take courses in Machine Learning (e.g., 601.475/675, 601.476/676), they may find it beneficial to take this course first, or concurrently. [Applications] Prereq: 601.226; Recommended: linear algebra, prob/stat. Students can only receive credit for one of 601.464/664. |
TuTh 4:30-5:45 |
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601.465 (E) |
NATURAL LANGUAGE PROCESSING (4) Eisner This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. [Applications] (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 |
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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. [Applications] 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 |
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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. [Applications] Required course background: prob/stat, 601.226. Student may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
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601.470 (E) NEW COURSE! |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with agents in games: how to build models that understand user commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, presentations on readings, and written summaries of readings. [Applications] Prereq: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
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601.475 (E) |
MACHINE LEARNING (3) Dredze, Liu
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.
[Applications or Analysis] Pre-reqs: multivariable calculus (110.202 or 110.211) & probability (553.310/553.311 or 553.420 or 560.348) & linear algebra (110.201 or 110.212 or 553.291) & intro computing (EN.500.112, EN.500.113, EN.500.114, EN.601.220 or AS.250.205). Students may receive credit for only one of 601.475/675. |
Sec 01 (Dredze): MWF 1:30-2:45, CS/CE only, limit 40 |
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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. [Analysis] 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. |
TuTh 3-4:15 |
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601.481 (EQ) CANCELED |
MACHINE LEARNING: OPTIMIZATION (3) Arora Optimization is at the heart of machine learning. Most machine learning problems can be posed as optimization problems. However, unlike mathematical optimization where the focus is on efficient algorithms for finding solutions with a high degree of accuracy as measured by optimality conditions, optimization for machine learning focuses on algorithms that are efficient and generalize well. In this course, we will focus on optimization for problems that arise in machine learning, design and analysis of algorithms for solving these problems, and the interplay of optimization and machine learning. The coursework will include homework assignments and a final project focusing on applying optimization algorithms to real world machine learning problems. [Analysis or Applications] Pre-requisites: 601.475 Machine Learning or
all of the following:
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MWF 12-1:15 |
|
EN.601.484 (E) NEW COURSE! |
ML: INTERPRETABLE MACHINE LEARNING DESIGN (3) Unberath There are considerable research thrusts that seek to increase the trustworthiness and perceived reliability of machine learning solutions. One such thrust, interpretable machine learning, attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, interpretability is not a property of machine learning algorithms, but an affordance: a relationship between interpretable model and the target users in their context. Successful development of machine learning solutions that afford interpretation thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, during student team-delivered lectures, we will learn about contemporary techniques to introduce interpretability to machine learning models and discuss recent literature on the topic. In addition to hands-on homework assignments, students will work in groups to design, justify, implement, and test an interpretable machine learning algorithm for a problem of their choosing. Pre-reqs: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
|
AS.050.375 (Q) |
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Was EN.601.485, now cross-listed as AS.050.375] 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. [Applications or Analysis] Pre-requisites: Calc I, programming experience (Python preferred). |
TuTh 9-10:15 |
|
601.490 (E) |
INTRO TO HUMAN-COMPUTER INTERACTION (3) C-M Huang, Ajaykumar
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. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Huang): TuTh 3-4:15, CS majors/minors, limit 30 |
|
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 below for 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 below for 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 below for faculty section numbers and whether to select 507 or 517. |
|
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 below for 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. |
Only for faculty specifically marked below. |
|
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 below for faculty section numbers |
|
601.556 |
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) 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. |
Section 1: Taylor |
|
601.614 |
COMPUTER NETWORKS (3) Ghorbani 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. [Systems] Required Course Background: C/C++ programming and data structures, or permission. Students can only receive credit for one of 601.414/614. |
MW 3-4:15 (CS + MSEM) |
|
601.615 |
DATABASES Yarowsky Same material as 601.415, for graduate students. [Systems] (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 |
|
601.618 |
OPERATING SYSTEMS Huang Same material as 601.418, for graduate students. [Systems] Required course background: Data Structures & Computer System Fundamentals. Students may receive credit for only one of 601.318/418/618. |
TuTh 1:30-2:45 |
|
601.620 NEW COURSE DESIGN |
PARALLEL COMPUTING FOR DATA SCIENCE (3) Burns This course studies parallelism in data science, drawing examples from data analytics, statistical programming, and machine learning. It focuses mostly on the Python programming ecosystem but will use C/C++ to accelerate Python and Java to explore shared-memory threading. It explores parallelism at all levels, including instruction level parallelism (pipelining and vectorization), shared-memory multicore, and distributed computing. Concepts from computer architecture and operating systems will be developed in support of parallelism, including Moore’s law, the memory hierarchy, caching, processes/threads, and concurrency control. The course will cover modern data-parallel programming frameworks, including Dask, Spark, Hadoop!, and Ray. The course will not cover GPU deep-learning frameworks nor CUDA. The course is suitable for second-year undergraduate CS majors and graduate students from other science and engineering disciplines that have prior programming experience. [Systems]
Required course background: 601.226 and 601.229 or equiv, familiarity with Python. |
MW 4:30-5:45pm |
|
601.621 |
OBJECT ORIENTED SOFTWARE ENGINEERING Darvish Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhu-oose.com) 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. |
MWF 4:30-5:20p |
|
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. [Systems] Course background: 601.220, 601.226 & 601.229 required; 601.230 or 601.231 recommended |
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. Required Background: data structures. |
MW 1:30-2:45pm |
|
601.633 |
INTRO ALGORITHMS Garg Same material as 601.433, for graduate students. [Analysis] Required Background: Data Structures and (Discrete Math or Automata/Computation Theory). Students may receive credit for only one of 601.433/633. |
TuTh 12-1:15p |
|
601.637 CANCELED |
FEDERATED LEARNING & ANALYTICS (3) Braverman Federated Learning (FL) is an area of machine learning where data is distributed across multiple devices and training is performed without exchanging the data between devices. FL can be contrasted with classical machine learning settings when data is available in a central location. As such, FL faces additional challenges and limitations such as privacy and communication. For example, FL may deal with questions of learning from sensitive data on mobile devices while protecting privacy of individual users and dealing with low power and limited communication. As a result, FL requires knowledge of many interdisciplinary areas such as differential privacy, distributed optimization, sketching algorithms, compression and more. In this course students will learn basic concepts and algorithms for FL and federated analytics, and gain hands-on experience with new methods and techniques. Students will gain understanding in reasoning about possible trade-offs between privacy, accuracy and communication. [Analysis] Required course background: 601.433/633, prob/stat, and (601.464/664 or 601.475/675). Students may receive credit for only one of 601.437/637. |
TuTh 12-1:15pm |
|
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. [Systems] Required course background: data structures and computer system fundamentals. Students may receive credit for only one of 601.340/440/640. |
TuTh 12-1:15 |
|
601.642 |
MODERN CRYPTOGRAPHYJain Same material as 601.442, for graduate students. [Analysis] Required course background: Probability & Automata/Computation Theory. |
MW 1:30-2:45 |
|
601.643 |
SECURITY AND PRIVACY IN COMPUTING Jois Same material as 601.443, for graduate students. [Applications]
Required course background: C programming and
computer system fundamentals. |
Lecture: Tu 1:30-2:40 (co-listed with 443) |
|
601.647 |
COMPUTATIONAL GENOMICS: SEQUENCES Langmead Same material as 601.447, for graduate students. [Applications] 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. [Applications] Students may receive credit for only one of EN.601.449, EN.601.649, EN.601.749. Prereq: Prereq: working knowledge of the Unix operating system and programming expertise in R or Python. |
MW 1:30-2:45 |
|
601.651 NEW COURSE! |
INTRODUCTION TO COMPUTATIONAL IMMUNOGENOMICS (3) Safonova Immunology studies defensive mechanisms of living organisms against external threats. Computational immunogenomics is a new field of bioinformatics that develops and applies computational approaches to the study and interpretation of immunological data, seeking to answer questions about adaptive immune responses in humans and important animals. In this course, students will learn how to design, apply, and benchmark algorithms for solving immunogenomics problems. [Applications] Required Course Background: Intermediate Programming & Data Structures. Students may receive credit for only one of EN.601.451, EN.601.651. |
TuTh 12-1:15p |
|
601.654 ADDED! |
AUGMENTED REALITY (3) Martin-Gomez
This course introduces students to the field of Augmented
Reality. It reviews its basic definitions, principles and
applications. It then focuses on Medical Augmented Reality and its
particular requirements. The course also discusses the main issues of
calibration, tracking, multi-modal registration, advance visualization
and display technologies. Homework in this course will relate to the
mathematical methods used for calibration, tracking and visualization
in medical augmented reality. Students may also be asked to read
papers and implement various techniques within group
projects. [Applications] Students may receive credit for 600.484 or 600.684, but
not both. Required course background: intermediate programming (C/C++), data structures, linear algebra. |
TuTh 3-4:15p |
|
601.655 |
COMPUTER INTEGRATED SURGERY I Taylor Same material as 601.455, for graduate students. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, computer graphics, computer vision, image processing. 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 1:30 |
|
601.661 |
COMPUTER VISION Katyal Same material as 601.461, for graduate students. Students may receive credit for at most one of 601.461/661/761. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Required course background: intro programming & linear algebra & prob/stat |
TuTh 12-1:15 |
|
601.662 (E) CANCELED! |
INTRODUCTION TO SPATIAL COMPUTING Azimi This course will provide students with a rich understanding of immersive technology and spatial computing, including virtual, augmented, and mixed reality as the next wave of computing after personal and mobile computing, and belongs to the systems that can sense the space or are “spatially” aware. It also covers input systems and interaction modalities that have evolved to support human-computer interaction required for immersive environments. It will go through principles of design thinking including verification and validation, as a mindset for creating immersive experiences, and students will explore the practical implication of this subject in healthcare, industry, and society through the projects. [Applications] Prereq: intermediate programming, data structures and linear algebra required; computer vision recommended. Students can earn credit for at most one of 601.462/662. |
|
|
601.663 |
ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard Same material as EN.601.463, for graduate students. [Analysis] Required course background: data structures & linear algebra & prob/stat. Students may receive credit for only one of 601.463/663/763. |
Sec 01: TuTh 3-4:15, WSE grads |
|
601.664 |
ARTIFICIAL INTELLIGENCE Haque Same as 601.464, for graduate students. [Applications] Prereq: Data Structures; Recommended: linear algebra & prob/stat. Students can only receive credit for one of 601.464/664. |
TuTh 4:30-5:45 |
|
601.665 |
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. [Applications] (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. [Applications] 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. [Applications] Required course background: prob/stat, data structures. Student may receive credit for at most one of 601.468/668. |
TuTh 1:30-2:45 |
|
601.670 NEW COURSE! |
ARTIFICIAL AGENTS (3) VanDurme This course covers a number of topics explored in introductory AI, such as knowledge representation, reasoning, and natural language understanding. Unlike introductory AI, we will pursue these topics based on the transformer neural architecture. We will motivate the material through interacting with agents in games: how to build models that understand user commands, how to generate responses back to a user, and how to reason about a synthetic environment to determine a course of action. Assignments will include programming, presentations on readings, and written summaries of readings. [Applications] Required Course Background: (Machine Learning, or Machine Learning: Deep Learning, or Machine Translation, or Artificial System Design and Development), or (experience with pytorch or related environment and instructor approval). [601.475/675 OR 601.482/682 OR 601.468/668 OR 601.486/686] Students may receive credit for at most one of 601.470/670. |
MW 8:30-9:45a |
|
601.675 |
MACHINE LEARNING Dredze, Liu
Same material as 601.475, for graduate students.
[Applications or Analysis] Required course background: multivariable calculus, probability, linear algebra, intro computing. Student may receive credit for only one of 601.475/675. |
Sec 01 (Dredze): MWF 1:30-2:45, CS only, limit 30 |
|
601.677 |
CAUSAL INFERENCE Shpitser Same material as 601.477, for graduate students. [Analysis] 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. |
TuTh 3-4:15 |
|
601.681 CANCELED |
MACHINE LEARNING: OPTIMIZATION Arora Same material as 601.481, for graduate students. [Analysis or Applications] Required course background: EN.601.475/675 Machine Learning or
all of the following:
|
MWF 12-1:15 |
|
EN.601.684 (E) NEW COURSE! |
ML: INTERPRETABLE MACHINE LEARNING DESIGN (3) Unberath There are considerable research thrusts that seek to increase the trustworthiness and perceived reliability of machine learning solutions. One such thrust, interpretable machine learning, attempts to reveal the working mechanisms of a machine learning system. However, other than on-task performance, interpretability is not a property of machine learning algorithms, but an affordance: a relationship between interpretable model and the target users in their context. Successful development of machine learning solutions that afford interpretation thus requires understanding of techniques beyond pure machine learning. In this course, we will first review the basics of machine learning and human-centered design. Then, during student team-delivered lectures, we will learn about contemporary techniques to introduce interpretability to machine learning models and discuss recent literature on the topic. In addition to hands-on homework assignments, students will work in groups to design, justify, implement, and test an interpretable machine learning algorithm for a problem of their choosing. Required course background: 601.475/675 or 601.464/664 or 601.482/682; coding in Python/PyTorch. Recommended (601.454/654, 601.290, 601.490/690 or 601.491/691) and 601.477/677. Students may receive credit for only one of 601.484/684. |
MW 4:30-5:45 |
|
AS.050.675 |
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Was EN.601.685, now cross-listed as AS.050.675.] 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. [Applications or Analysis] Pre-requisites: Calc I, programming experience (Python preferred). |
TuTh 9-10:15 |
|
601.690 |
INTRO TO HUMAN-COMPUTER INTERACTION C-M Huang, Ajaykumar
Same material as EN.601.490, for graduate students. [Applications] Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. |
Sec 01 (Huang): TuTh 3-4:15, limit 10, CS only |
|
601.713 NEW COURSE! |
FUTURE NETWORKS Sabnani
This will be a graduate-level networking course. New applications such as ones for metaverse require networking and computing to be imbedded together. This feature is already beginning to be implemented in 5G and 6G networks; 6G will also allow networks to be used as sensors. These advances are enabled by new technologies such as mobile edge computing, software-defined networking (SDN), network slicing, digital twins, and named-data networking (NDN). This course will start with introductory lectures on these topics. Students will be asked to study new papers and do course projects. These activities should result in longer term research projects. [Systems] |
Tu 4:30-7p |
|
601.771 NEW COURSE! |
SELF-SUPERVISED STATISTICAL MODELS: OPPORTUNITIES, CHALLENGES AND RISKS Khashabi
The rise of massive self-supervised (pre-trained) models has
transformed various data-driven fields such as natural language
processing, computer vision, robotics, and medical imaging. This
advanced graduate course aims to provide a holistic view of the issues
related to these models: We will start with the history of how we got
here, and then delve into the latest success stories. We will then
focus on the implications of these technologies: social harms,
security risks, legal issues, and environmental impacts. The class
ends with reflections on the future implications of this trajectory.
|
TuTh 1:30-2:45 |
|
601.801 |
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 below for faculty section
numbers. |
|
601.805 |
GRADUATE INDEPENDENT STUDY Permission required. |
See below for faculty
section numbers. |
|
601.807 |
TEACHING PRACTICUM 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 25 |
|
601.809 |
PHD RESEARCH Independent research for PhD students. |
See below for faculty section
numbers. |
|
601.810 |
DIVERSITY & INCLUSION IN COMPUTER SCIENCE & ENGINEERING Kazhdan This reading seminar will focus on the question of diversity and inclusion in computer science (in particular) and engineering (in general). We aim to study the ways in which the curriculum, environment, and structure of computer science within academia perpetuates biases alienating female and minoritized students, and to explore possible approaches for diversifying our field. The seminar will meet on a weekly basis, readings will be assigned, and students will be expected to participate in the discussion. |
Wed 4:30-5:45, limit 8 |
|
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 |
|
601.817 |
SELECTED TOPICS IN SYSTEMS RESEARCH R.Huang This course covers latest advances in the research of computer systems including operating systems, distributed system, mobile and cloud computing. Students will read and discuss recent research papers in top systems conferences. Each week, one student will present the paper and lead the discussion for the week. The focus topics covered in the papers vary semester to semester. Example topics include fault-tolerance, reliability, verification, energy efficiency, and virtualization. |
Fr 1:30-2:45 |
|
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. |
Fr 11-12 |
|
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. |
T 3p |
|
601.862 NEW COURSE! |
SELECTED TOPICS IN MEDICAL IMAGE PROCESSING Jones This course will provide a background in medical imaging modalities and the unique aspects of image processing as it pertains to medical imaging. We will cover what an image is, how it is formed through six imaging modalities, and how images are typically stored, as well as background topics such as image metrics, quantification, filtering and transforms. More advanced topics will be discussed such as visualization, image enhancement, segmentation and registration. The final few weeks will introduce the topic of neural networks in image processing. Students will be expected to read and discuss publications, as well as complete an implementation project and report. Recommended course background: programming & linear algebra. |
Th 3p |
|
601.864 NEW COURSE! |
SELECTED TOPICS IN MULTILINGUAL NATURAL LANGUAGE PROCESSING 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 3p |
|
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. Enrolled students are expected to present papers and lead discussion. |
W 12-1:15p |
|
601.866 |
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. |
Fr 10:45-11:45 |
|
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 |
|
520.702 |
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING staff CLSP seminar series, for any students interested in current topics in language and speech processing. |
Tu & Fr 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 |
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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 6:40-9:10p |
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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. |
TuTh 3-4:15p |
|
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. |
Fr 4:15-6:45 |
|
650.683 |
CYBERSECURITY RISK MANAGEMENT Tom McGuire 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.
|
MW 1:30-2:45p |
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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. |
MWF 11 |
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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.
01 - Xin Li 02 - Rao Kosaraju (emeritus) 03 - Soudeh Ghorbani 04 - Russ Taylor (ugrad research use 517, not 507) 05 - Scott Smith 06 - Joanne Selinski 07 - Harold Lehmann [SPH] 08 - Ali Madooei 09 - Greg Hager (ugrad research use 517, not 507) 10 - staff 11 - Sanjeev Khudhanpur [ECE] 12 - Yair Amir 13 - David Yarowsky 14 - Noah Cowan 15 - Randal Burns 16 - Jason Eisner (ugrad research use 517, not 507) 17 - Mark Dredze 18 - Michael Dinitz 19 - Rachel Karchin [BME] 20 - Michael Schatz 21 - Avi Rubin 22 - Matt Green 23 - Yinzhi Cao 24 - Raman Arora (ugrad research use 517, not 507) 25 - Rai Winslow [BME] 26 - Misha Kazhdan 27 - David Hovemeyer 28 - Ali Darvish 29 - Alex Szalay [Physics] 30 - Peter Kazanzides 31 - Jerry Prince [BME] 32 - Carey Priebe [AMS] 33 - Nassir Navab 34 - Rene Vidal [BME] 35 - Alexis Battle (ugrad research use 517, not 507) [BME] 36 - Emad Boctor (ugrad research use 517, not 507) [SOM] 37 - Mathias Unberath 38 - Ben VanDurme 39 - Jeff Siewerdsen 40 - Vladimir Braverman 41 - Suchi Saria 42 - Ben Langmead 43 - Steven Salzberg 44 - Jean Fan [BME] 45 - Liliana Florea [SOM] 46 - Casey Overby Taylor [SPH] 47 - Philipp Koehn 48 - Abhishek Jain 49 - Anton Dahbura (ugrad research use 517, not 507) 50 - Joshua Vogelstein [BME] 51 - Ilya Shpitser 52 - Austin Reiter 53 - Tamas Budavari [AMS] 54 - Alan Yuille 55 - Peng Ryan Huang 56 - Xin Jin 57 - Chien-Ming Huang 58 - Will Gray Roncal (ugrad research use 517, not 507) 59 - Kevin Duh [CLSP] 60 - Mihaela Pertea [BME] 61 - Archana Venkataraman [ECE] 62 - Matt Post [CLSP] 63 - Vishal Patel [ECE] 64 - Rama Chellappa [ECE] 65 - Mehran Armand [MechE] 66 - Jeremias Sulam [BME] 67 - Anqi Liu 68 - Yana Safanova 69 - Musad Haque 70 - Stephen Walli 71 - Gregory Falco [CaSE] 72 - Thomas Lippincott [CLSP] 73 - Joel Bader [BME] 74 - Daniel Khashabi 75 - Nicolas Loizou (AMS) 76 - Alejandro Martin Gomez 77 - Kenton Murray 78 - Ehsan Azimi [SoN] 79 - Krishan Sabnani