Fall 2023 Courses

601.104 (H)
CSCI-ETHS

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.
Sections meet during the first 8 weeks of the semester only.

Sec 01: Mon 4:30-6:00p
Sec 02: Mon 6:30-8:00p
Sec 03: Tue 4:30-6:00p
Sec 04: Tue 6:30-8:00p
limit 19 each, CS majors only (no expiration)

601.124 (EH)
CSCI-ETHS

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.
This new course may be used as an alternative course to satisfy the CS Ethics requirement.

Sec 01: MW 12-1:15p
Sec 02: MW 3-4:15p
limit 19 each, CS majors only (no expiration)

601.220 (E)

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 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
Sec 01 (Simari): MWF 10-11:15am
Sec 02 (Darvish): MWF 12-1:15pm, incoming first-years only
Sec 03 (Darvish): MWF 1:30-2:45p, incoming first-years only
Sec 04 (Simari): MWF 3:00-4:15pm
limit 35/sections 1-3; limit 70 section 4

601.226 (EQ)

DATA STRUCTURES (4) Madooei & Simari

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.

Sec 01 (Madooei): MWF 12-1:15pm, limit 100
Sec 02 (Simari): MWF 1:30-2:45pm, limit 100
CS/CE majors/minors + CIS/Robotics minors

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
Sec 02: MWF 10-10:50am, limit 90
CS/CE majors/minors

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
Sec 02: MWF 9-9:50, W 4:30-5:20
Sec 03: MWF 9-9:50, W 6-6:50
Sec 04: MWF 9-9:50, W 6-6:50
Sec 05: MWF 9-9:50, Th 9-9:50
Sec 06: MWF 9-9:50, Th 10:30-11:20
Sec 07: MWF 9-9:50, Th 4:30-5:20
Sec 08: MWF 9-9:50, Th 4:30-5:20
limit 19/section, CS/CE majors only

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
Sec 01: limit 60, CS majors
Sec 02: limit 30, CS sophomore majors

601.315 (E)
CSCI-SOFT

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
limit 35, CS/CE majors/minors

601.340 (E)
CSCI-SYST
Overview Video

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
limit 20, CS/CE majors/minors

660.410 (E)
CSCI-OTHR

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.]
This course is designed to give students in CS the requisite skills to generate and screen ideas for new venture creation and then prepare a business plan for an innovative technology of their own design. These skills include the ability to incorporate into a formal business case all necessary requirements, including needs identification and validation; business and financial models; and, market strategies and plans. Student teams will present the business plan to an outside panel made up of practitioners, industry representatives, and venture capitalists. In addition, this course functions as the first half of a two course sequence, the second of which will be directed by CS faculty and focus on the actual construction/programming of the business idea.
Restricted to Juniors and Seniors majoring in Computer Science or by permission of instructor.

MW 12-1:15
limit 19

601.414 (E)
CSCI-SYST

COMPUTER NETWORKS (3) staff

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.

TuTh 3-4:15p
limit 24, CS/CE majors/minors

601.415 (E)
CSCI-SOFT

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
limit 12, CS/CE majors/minors

601.420 (E)
CSCI-SYST

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
limit 45, CS/CE majors/minors

601.421 (E)
CSCI-SOFT, CSCI-TEAM

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: 601.220, 601.226, and (EN.601.280 or EN.601.290). Students may receive credit for only one of 601.421/621.

MWF 3-3:50p
Sec 01: limit 50, pre-reqs enforced;
Sec 02: limit 15, instructor approval only, pre-reqs not enforced;
CS/CE majors/minors

601.425 (E)
CSCI-SOFT
NEW COURSE!

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 Databases, or permission. Students may receive credit for only one of 601.425/625.

MWF 4:30-5:20p
Sec 01: limit 25, pre-reqs enforced;
Sec 02: limit 5, instructor approval only, pre-reqs not enforced;
CS majors only

601.428 (E)
CSCI-SOFT

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: 601.226 & 601.229 required; 601.230 or 601.231 recommended

MW 12-1:15pm
limit 24, CS/CE majors/minors

601.429 (E)
CSCI-SOFT

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
limit 34, CS/CE majors/minors

601.431 (E,Q)
CSCI-THRY

NEW COURSE!

THEORY OF COMPUTATION (3) Li

This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad.

Prereq: discrete math or EN.601.230 or permission. Discrete probability recommended. Students can receive credit for only one of EN.601.431/EN.601.631.

TuTh 1:30-2:45
limit 19, CS/CE majors/minors

601.433 (EQ)
CSCI-THRY
Sample Syllabus

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 & (553.171/172 or 601.230 or 601.231). Students may receive credit for only one of 601.433/633.

Sec 01: TuTh 1:30-2:45pm, limit 135
Sec 02: TuTh 12-1:15pm, limit 55
CS/CE majors/minors

601.440 (E)
CSCI-SYST
Overview Video

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
limit 9, CS/CE majors/minors

601.443 (E)
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING (3) staff

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. Students may receive credit for only one of 601.443/643.

TuTh 9-10:15a
limit 15, CS/CE majors/minors

601.447 (E)
CSCI-APPL, CSCI-TEAM
Overview Video

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
Sec 01: limit 30, CS/CE majors/minors
Sec 02: limit 5, CompMed minor

601.449
CSCI-APPL

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]

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
Sec 01: limit 14, CS only
[Sec 02: limit 5, instructor approval, closed for now]

601.451 (E)
CSCI-APPL
CANCELLED

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
Sec 01: limit 15, CS only
Sec 02: limit 5, CompMed minor

601.454 (E) CSCI-APPL

INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez

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. [Applications]

Prerequisites: EN.601.220, EN.601.226, linear algebra. Students may receive credit for only one of 601.454/654.

TuTh 3-4:15p
Sec 01: limit 10, CS/CE majors/minors
Sec 02: limit 4, CIS+Robotics minors

601.455 (E)
CSCI-APPL

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
Sec 01: limit 25, CS/CE majors/minors + CompMed/CIS/Robotics minors
[Sec 02: limit 5, instructor approval, closed for now]

601.457 (EQ)
CSCI-APPL

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.

MWF 11
Sec 01: limit 20, CS/CE majors/minors
Sec 02: limit 5, CIS+Robotics minors

601.461 (EQ)
CSCI-APPL

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
Sec 01: limit 35, CS/CE majors/minors
Sec 02: limit 5, CompMed/CIS/Robotics minors

601.463 (E)
CSCI-APPL

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, Calc III, linear algebra & probability. Students may receive credit for only one of 601.463/663/763.

Sec 01: TuTh 3-4:15
Sec 02: TuTh 4:30-5:45
limit 9/section, CS/CE majors/minors + CIS/Robotics minors

601.464 (E)
CSCI-RSNG

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.

MW 1:30-2:45
Sec 01: limit 95, CS/CE majors/minors
Sec 02: limit 5, CIS/Robotics minors

601.465 (E)
CSCI-APPL
Sample Syllabus

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. [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
Section: Tu 6-7:30p
limit 35, CS/CE majors/minors

601.467 (E)
CSCI-APPL

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
limit 20, CS/CE majors/minors

601.468 (E)
CSCI-APPL

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. Students may receive credit for at most one of 601.468/668.

TuTh 1:30-2:45
limit 20, CS/CE majors/minors

601.475 (E)
CSCI-RSNG

MACHINE LEARNING (3) Arora

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 (calc III), prob/stat, linear algebra, intro computing. Students may receive credit for only one of 601.475/675.

MWF 12-1:15p
Sec 01: limit 60, CS/CE majors/minors
Sec 02: limit 5, CompMed/CIS/Robotics minors

601.477 (EQ)
CSCI-RSNG

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
limit 10, CS/CE majors/minors

601.482 (E)
CSCI-RSNG

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.
The goal of this course is to introduce the basic concepts of deep learning (DL). The course will include a brief introduction to the basic theoretical and methodological underpinnings of machine learning, commonly used architectures for DL, DL optimization methods, DL programming systems, and specialized applications to computer vision, speech understanding, and robotics.
Students will be expected to solve several DL problems on standardized data sets, and will be given the opportunity to pursue team projects on topics of their choice. [Applications]

Pre-req: (AS.110.201 or AS.110.212 or EN.553.291) and (EN.553.310 EN.553.311 or EN.553.420 or EN.560.348) and (EN.601.475 or equiv) and Calc III; numerical optimization and Pythonrecommended.

MW 4:30-5:45p, F 4:30-5:20p
Sec 01: limit 45, CS/CE majors/minors
Sec 02: limit 5, CompMed/CIS/Robotics minors

601.485 (Q)
CSCI-APPL

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.
To seek approval, request enrollment in the course. You'll be added to a 'Pending Enrollments' list. Then, take the placement test. Link TBA.

TuTh 9-10:15
limit 15
instructor approval only

601.486 (E)
CSCI-SOFT, CSCI-TEAM

MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT (3) Dredze

Advances in Artificial intelligence have opened new opportunities for developing systems to aid in numerous areas of society. In order for AI systems to succeed in making constructive and positive changes, we must consider their impact on everyday life. Specifically, AI system designers must evaluate the overall capabilities of the system, consider the resulting human-AI interactions, and ensure that the system behaves in a responsible and ethical manner. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Articulate principles of Responsible AI relevant to the intended application, 3) Conceptualize and design an AI system targeting this need, and 4) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations. Additionally, we will discuss potential ethical issues that can arise in AI and how to develop Responsible AI principles. Coursework will consist of writing assignments, project presentations, and a project demonstration.

Pre-req: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).

MW 1:30-2:45
limit 15

601.490 (E)
CSCI-SOFT, CSCI-TEAM

INTRO TO HUMAN-COMPUTER INTERACTION (3) C-M Huang & 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. [Applications]

Pre-req: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both.

Sec 01: TuTh 3-4:15, limit 30, CS majors/minors
Sec 02: TuTh 3-4:15, limit 10, KSAS students
Sec 03: Th 4:30-7p, limit 40, CS majors only [added]

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

601.513
NEW COURSE!

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.

Only for faculty specifically marked below.

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
CSCI-SYST

COMPUTER NETWORKS (3) staff

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.

TuTh 3-4:15p
limit 24, CS+MSEM

601.615
CSCI-SOFT

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
Sec 01: limit 40, CS+MSSI
Sec 02: limit 5, MSEM+Data Science

601.620
CSCI-SYST

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
Sec 01: limit 35, CS
Sec 02: limit 10, MSEM + Data Science

601.621
CSCI-SOFT

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 3-3:50p
Sec 01: limit 25, instructor approval only

601.625
CSCI-SOFT
NEW COURSE!

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.

Required course background: EN.601.315/415/615 Databases, or permission. Students may receive credit for only one of 601.425/625.

MWF 4:30-5:20p
Sec 01: limit 15, instructor approval CS grads only

601.628
CSCI-SOFT

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]

Expected course background: 601.220, 601.226 & 601.229 required; 601.230 or 601.231 recommended

MW 12-1:15
limit 24, CS+MSEM

601.629
CSCI-SOFT

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 course background: data structures. Students can receive credit for only one of EN.601.429/EN.601.629.

MW 1:30-2:45pm
limit 34, CS+MSEM

601.631
CSCI-THRY

THEORY OF COMPUTATION (3) Li

This course covers the theoretical foundations of computer science. Topics included will be models of computation from automata to Turing machines, computability, complexity theory, randomized algorithms, inapproximability, interactive proof systems and probabilistically checkable proofs. Students may not take both 601.231 and 601.431/601.631, unless one is for an undergrad degree and the other for grad. [Analysis]

Required Background: discrete math or permission; discrete probability theory recommended. Students can receive credit for only one of EN.601.431/EN.601.631.

TuTh 1:30-2:45
limit 29, CS+MSEM

601.633
CSCI-THRY
Sample Syllabus

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
Sec 01: limit 50, CS+MSSI
Sec 02: limit 30, MSEM+Robotics+DataSci

601.640
CSCI-SYST
Overview Video

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, computer system fundamentals and javascript/web development. Students may receive credit for only one of 601.340/440/640.

TuTh 12-1:15
limit 40, CS+MSEM+MSSI

601.643
CSCI-SOFT

SECURITY AND PRIVACY IN COMPUTING staff

Same material as 601.443, for graduate students. [Applications]

Required course background: C programming and computer system fundamentals.

TuTh 9-10:15a
limit 33, CS+MSEM+MSSI

601.647
CSCI-APPLOverview Video

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
Sec 01: limit 20, CS
Sec 02: limit 5, MSEM + Data Science + Non-ASEN
[Sec 03: limit 5, instructor approval, closed for now]

601.649
CSCI-APPL

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]

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
Sec 01: limit 15, CS only
[Sec 02: limit 10, instructor approval, closed for now]

601.651
CSCI-APPL
CANCELLED

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
Sec 01: limit 15, CS only
Sec 02: limit 6, Data science + Non-ASEN grads
[Sec 03: limit 8, instructor approval, closed for now]

601.654
CSCI-APPL

INTRODUCTION TO AUGMENTED REALITY (3) Martin-Gomez

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. [Applications]

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
Sec 01: limit 14, CS grads
Sec 02: limit 8, Robotics & MSEM

601.655
CSCI-APPL

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
Sec 01: limit 10, CS
Sec 02: limit 30, Robotics
Sec 03: limit 10, WSE + Non-ASEN grads
[Sec 04: limit 10, instructor approval, closed for now]

601.657
CSCI-APPL

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
limit 20, CS+MSEM+Robotics

601.661
CSCI-APPL

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
Sec 01: limit 40, CS+MSEM
Sec 02: limit 20, Robotics + Data Science
[Sec 03: limit 10, instructor approval, closed for now]

601.663
CSCI-APPL

ALGORITHMS FOR SENSOR-BASED ROBOTICS Leonard

Same material as EN.601.463, for graduate students. [Analysis]

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 3-4:15, limit 30, WSE + Non-ASEN grads
Sec 02: TuTh 4:30-5:45, limit 20, CS grads

601.664
CSCI-RSNG

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.

MW 1:30-2:45
Sec 01: limit 60, CS + MSEM grads
Sec 02: limit 30, Robotics + Data Science grads
[Sec 03: limit 5, instructor approval, closed for now]

601.665
CSCI-APPL
Sample Syllabus

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
Section: Tu 6-7:30p
Sec 01: limit 50, CS & HLT only
Sec 02: limit 5, Data Science only
[Sec 03: limit 10, instructor active approval, closed for now]

601.667 (E)
CSCI-APPL

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
Sec 01: limit 60, CS + HLT only
[Sec 02: limit 10, instructor approval, closed for now]

601.668
CSCI-APPL

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
Sec 01: limit 60, CS + HLT only
Sec 02: limit 5, MSEM + Data Science
[Sec 03: limit 10, instructor approval, closed for now]

601.675
CSCI-RSNG

MACHINE LEARNING Arora

Same material as 601.475, for graduate students. [Applications or Analysis]

Required course background: multivariable calculus (calc III), prob/stat, linear algebra, intro computing. Student may receive credit for only one of 601.475/675.

MWF 12-1:15p
Sec 01: limit 40, CS + MSEM grads
Sec 02: limit 30, Robotics + Data Science masters
[Sec 03: limit 10, instructor approval, closed for now]

601.677
CSCI-RSNG

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
Sec 01: limit 29, CS grads
Sec 02: limit 10, MSEM + Data Science + AMS + Non-ASEN
[Sec 03: limit 10, instructor approval, closed for now]

601.682
CSCI-RSNG

MACHINE LEARNING: DEEP LEARNING Unberath

Same as 601.482, for graduate students. [Applications]

Required course background: probability and linear algebra, some machine learning; calc III and numerical optimization recommended.

MW 4:30-5:45p, F 4:30-5:20p
Sec 01: limit 60, CS + MSEM grads
Sec 02: limit 20, Robotics + Data Science masters
[Sec 03: limit 10, instructor approval, closed for now]

EN.601.685
CSCI-APPL

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. [Applications or Analysis]

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.
To seek approval, request enrollment in the course. You'll be added to a 'Pending Enrollments' list. Then, take the placement test. Link TBA.

TuTh 9-10:15
limit 20
instructor approval

601.686
CSCI-SOFT

MACHINE LEARNING: ARTIFICIAL INTELLIGENCE SYSTEM DESIGN & DEVELOPMENT Dredze

Advances in Artificial intelligence have opened new opportunities for developing systems to aid in numerous areas of society. In order for AI systems to succeed in making constructive and positive changes, we must consider their impact on everyday life. Specifically, AI system designers must evaluate the overall capabilities of the system, consider the resulting human-AI interactions, and ensure that the system behaves in a responsible and ethical manner. In this project-based course you will work in teams of 3-5 students to 1) Identify a need with high-impact implications on everyday life; 2) Articulate principles of Responsible AI relevant to the intended application, 3) Conceptualize and design an AI system targeting this need, and 4) Develop the AI system by refining a demo-able prototype based on feedback received during course presentations. Additionally, we will discuss potential ethical issues that can arise in AI and how to develop Responsible AI principles. Coursework will consist of writing assignments, project presentations, and a project demonstration.

Required course background: (EN.601.475/675 or EN.601.464/664 or EN.601.482/682) and Python programming. Recommended: 601.290 or 601.454/654 or 601.490/690 or 601.491/691 (experience with human computer interface design).

MW 1:30-2:45
limit 14

601.690
CSCI-SOFT

INTRO TO HUMAN-COMPUTER INTERACTION C-M Huang & Reiter

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: TuTh 3-4:15, limit 10, CS only
Sec 02: TuTh 3-4:15, limit 10, instructor approval
Sec 03: Th 4:30-7p, limit 20, CS only [added]

601.713
CSCI-SYST

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]
Required Course Background: A course in computer networks (e.g., EN.601.414/614 Computer Network Fundamentals or the equivalent), or permission of the instructor.

Tu 4:30-7p
limit 25, CS grads

601.742
CSCI-THRY
CANCELLED

ADVANCED TOPICS IN CRYPTOGRAPHY (3) Jain

[Cross-listed in ISI] This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations. [Analysis]

Prereq: 601.442/642 or permission.

Wed 1:30-4:15p
limit 19

601.770
CSCI-APPL
NEW COURSE!

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. [Applications]
Required Course Background: At least one graduate-level computer science course in Artificial Intelligence or Machine Learning (including NLP, Computer Vision, etc.), two preferred, or permission of the instructor.

TuTh 12-1:15p
limit 29, CS grads

601.801

COMPUTER SCIENCE SEMINAR

Attendance recommended for all grad students; only 1st & 2nd year PhD students may register.

TuTh 10:30-12
limit 90

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 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 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

601.811

FUTURE FACULTY: Preparing a New Generation of PIs for the Academic Job Search Unberath

The goal of this seminar-style course is to prepare senior PhD students and postdocs in CS and robotics adjacent disciplines for the academic job market. At the end of the course sequence, it is expected that participants will 1) understand benefits and possible challenges of the academic career path, 2) be familiar with many aspects of the academic job market (such as timing, required documents, interview schedule, …), 3) have completed a first draft of their application documents to be further refined with their respective advisors and mentors, 4) be prepared to tackle phone and on-campus interviews, and 5) have an appreciation for the essential tasks junior faculty must master quickly.

Th 1:30-2:45p
limit 30

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.

Fr 11-12
limit 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
limit 12

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 3p
limit 15

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.
Required course background: EN.601.465/665 or permission of instructor.

W 12-1:15p
limit 15

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
limit 15

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
limit 15

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.

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
limit 80

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
limit 25

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
limit 25, MSSI only

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
limit 30

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
limit 55

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.

TuTh 1:30-2:45p
limit 50, instructor approval

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
limit 25, MSSI + CS

650.663

CANCELLED

CLOUD COMPUTING SECURITY Chris Monson

F 1:30-4pm
limit 25, MSSI + CS

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
limit 30, MSSI + CS

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.
Recommended Course Background: EN.650.601.

MW 1:30-2:45p
limit 35, MSSI

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
limit 55, MSSI

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.