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 nondepartment courses that may be used as "CS other" in accordance with established credit restrictions.
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 4/28/2021 for most undergraduate courses and 8/1/2021 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:
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 was completed. [All colisted 601.4xx/6xx courses are equivalent.]
Teaching Modes  The university is currently planning to move into Phase III modalities, meaning that most courses <50 people will be taught in person, but those with >=50 (combined enrollment) will still be conducted virtually. For CS, those that will be taught on campus are indicated with an "in person" tag below. You should assume that the other CS courses will be taught online, and synchronously in most cases. You should plan to attend class meetings as scheduled.
Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on SIS and the Registrar's website in September. Changes to the original SIS schedule will be noted in red.
500.112 (E) 
GATEWAY COMPUTING: JAVA (3) staff This course introduces fundamental programming concepts and techniques, and is intended for all who plan to develop computational artifacts or intelligently deploy computational tools in their studies and careers. Topics covered include the design and implementation of algorithms using variables, control structures, arrays, functions, files, testing, debugging, and structured program design. Elements of objectoriented programming, algorithmic efficiency and data visualization are also introduced. Students deploy programming to develop working solutions that address problems in engineering, science and other areas of contemporary interest that vary from section to section. Course homework involves significant programming. Attendance and participation in class sessions are expected. Sections start on the hour, from 8a  4p. Sections 2 & 8 (9a & 3p) are restricted to incoming CS majors. 
MWF 50 minutes, limit 19/section 
601.104 (H) 
COMPUTER ETHICS (1) Leschke Students will examine a variety of topics regarding policy, legal, and moral issues related to the computer science profession itself and to the proliferation of computers in all aspects of society, especially in the era of the Internet. The course will cover various general issues related to ethical frameworks and apply those frameworks more specifically to the use of computers and the Internet. The topics will include privacy issues, computer crime, intellectual property law  specifically copyright and patent issues, globalization, and ethical responsibilities for computer science professionals. Work in the course will consist of weekly assignments on one or more of the readings and a final paper on a topic chosen by the student and approved by the instructor. 
Sec 01: Wed 4:306:30p, alternate weeks starting 9/1 
601.220 (E)

INTERMEDIATE PROGRAMMING (4) staff This course teaches intermediate to advanced programming, using C and C++. (Prior knowledge of these languages is not expected.) We will cover lowlevel programming techniques, as well as objectoriented 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 majors/minors only 
601.226 (EQ) 
DATA STRUCTURES (4) Madooei This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments. Prereq: AP CS or >= C+ grade in 601.107, 601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission. 
Sec 01: MWF 121:15pm, limit 75 
601.229 (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Hovemeyer We study the design and performance of a variety of computer systems from simple 8bit microcontrollers through 32/64bit RISC architectures all the way to ubiquitous x86 CISC architecture. We'll start from logic gates and digital circuits before delving into arithmetic and logic units, registers, caches, memory, stacks and procedure calls, pipelined execution, superscalar architectures, memory management units, etc. Along the way we'll study several typical instruction set architectures and review concepts such as interrupts, hardware and software exceptions, serial and other peripheral communications protocols, etc. A number of programming projects, frequently done in assembly language and using various processor simulators, round out the course. Prereq: 601.220. 
01: MWF 99:50am, limit 49, in person 
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, NPcompleteness, and randomization. Students may not receive credit for 601.231 and 601.631 for the same degree. Prereq: 550/553.171/172. 
Sec 01: Tu 910:15am, Th 910:20am 
601.280 (E) 
FULLSTACK JAVASCRIPT (3) Madooei A fullstack 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 fullstack 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, crossplatform desktop, and native/hybrid mobile applications. A student who successfully completes this course will be on the expedited path to becoming a fullstack JavaScript developer. Prereq: 601.220 or 601.226. Students must not have taken or be concurrently enrolled in 601.421/621 OOSE. 
TuTh 121:15pm 
601.315 (E) 
DATABASES (3) Yarowsky Introduction to database management systems and database design, focusing on the relational and objectoriented 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 34:15pm 
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, multiprogramming 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 1:302:45pm 
601.320 (E) 
PARALLEL PROGRAMMING (3) Burns This course prepares the programmer to tackle the massive data sets and huge problem size of modern scientific and enterprise computing. Google and IBM have commented that undergraduate CS majors are unable to "break the single server mindset" (http://www.google.com/intl/en/ press/pressrel/20071008_ibm_univ.html). Students taking this course will abandon the comfort of serial algorithmic thinking and learn to harness the power of cuttingedge software and hardware technologies. The issue of parallelism spans many architectural levels. Even ``single server'' systems must parallelize computation in order to exploit the inherent parallelism of recent multicore processors. The course will examine different forms of parallelism in four sections. These are: (1) massive dataparallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) threadlevel parallelism in Java; and, (4) GPGPU parallel programming with NVIDIA's Cuda. Each section will be approximately 3 weeks and each section will involve a programming project. The course is also suitable for undergraduate and graduate students from other science and engineering disciplines that have prior programming experience. [Systems] Prereq: 601.226 and 601.229. Students may receive credit for at most one of 601.320/420/620. 
MW 4:305:45pm 
601.340 (E)

WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as sameorigin policy, crossorigin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as crosssite 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. Recommended: 601.280. Students may receive credit for only one of 601.340/440/640. 
TuTh 121:15 
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 34:15pm 
601.417 (E) 
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive handson experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses) Prereq: 601.220 & 601.226. Students may receive credit for only one of 601.417/617. 
TuTh 34:15pm 
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 1:302:45pm 
601.420 (E) 
PARALLEL PROGRAMMING (3) Burns More advanced version of 601.320. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. 
MW 4:305:45pm 
601.421 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Darvish This course covers objectoriented 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 objectoriented analysis and design, UML, design patterns, refactoring, program testing, code repositories, team programming, and code reviews. [(Systems or Applications), Oral] (https://www.jhuoose.com) Prereq: 600/601.226 & 600.120/601.220. Strongly recommended: EN.601.280 or EN.601.290 (will be requirement in future semesters). Students may receive credit for only one of 601.421/621. 
Lec: Tu 1:302:45pm 
601.428 (E) 
COMPILERS & INTERPRETERS (3) Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntaxdirected translation, symbol tables, runtime 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; 601.231 recommended 
MW 121:15pm 
601.429 (E) 
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith How can we effectively use functional programming techniques to build realworld 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. Prereq: 601.226 or instructor permission. 
MW 1:302:45pm 
601.430 (EQ) 
COMBINATORICS AND GRAPH THEORY IN CS Li This course covers the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Prerequisite: 550.171/553.171/553.172; probability theory and linear algebra recommended. Students may receive credit for only one of EN.601.430 and EN.601.630. 
TuTh 1:302:45pm 
601.433 (EQ) 
INTRO ALGORITHMS (3) Dinitz 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 unionfind); graph . algorithms and searching techniques such as minimum spanning trees, depthfirst search, shortest paths, design of online algorithms and competitive analysis. [Analysis] Prereq: 601.226 & (550/553.171/172 or 601.231) or Perm. Required. Students may receive credit for only one of 601.433/633. 
TuTh 121:15pm 
601.437 (EQ) NEW COURSE 
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 handson experience with new methods and techniques. Students will gain understanding in reasoning about possible tradeoffs 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 121:15pm 
601.440 (E)

WEB SECURITY (3) Cao This course begins with reviewing basic knowledge of the World Wide Web, and then exploring the central defense concepts behind Web security, such as sameorigin policy, crossorigin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as crosssite 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. Recommended: 601.280. Students may receive credit for only one of 601.340/440/640. 
TuTh 121:15pm 
601.443 (E) 
SECURITY AND PRIVACY IN COMPUTING (3) Rubin 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 semesterlong project that will be done in teams and will include a presentation by each group to the class. [Applications] Prerequisite: 601.229 & (601.318/418 or 601.414) Students may receive credit for only one of 601.443/643. 
Tu 910:10, Th 910:20 
601.445 (E)

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green & Chator This semesterlong course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widelydeployed cryptographic products and protocols. Our focus will be on the techniques used in practical security systems, the mistakes that lead to failure, and the approaches that might have avoided the problem. We will place a particular emphasis on the techniques of provable security and the feasibility of reverseengineering undocumented cryptographic systems. [Systems] Prereq: 601.226 & 601.229. Students may receive credit for only one of 601.445/645. 
MW 121:15 
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, realworld 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 nprojects. [Applications, Oral] Prereq: 601.220 & 601.226. Students may receive credit for at most one of 601.447/647/747. 
TuTh 910:15am 
601.452 (E) 
COMPUTATIONAL BIOMEDICAL RESEARCH (3) Schatz
[Colisted with AS.020.415] This course for advanced undergraduates includes classroom instruction in interdisciplinary research approaches and lab work on an independent research project in the lab of a Bloomberg Distinguished Professor and other distinguished faculty. Lectures will focus on crosscutting techniques such as data visualization, statistical inference, and scientific computing. In addition to two 50minute classes per week, students will commit to working approximately 3 hours per week in the lab of one of the professors. The student and professor will work together to schedule the research project. Students will present their work at a symposium at the end of the semester. Prereq: permission required. 
MW 33:50pm 
601.455 (E) 
COMPUTER INTEGRATED SURGERY I (4) Taylor This course focuses on computerbased 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 followup. It emphasizes the relationship between problem definition, computerbased 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 1:302:45pm 
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 prerequisite. Students may receive credit for only one of 601.457/657.

MWF 11 
601.461 (EQ) 
COMPUTER VISION (3) Leonard 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 121:15 
601.463 (E) 
ALGORITHMS FOR SENSORBASED 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 humanmachine systems. [Analysis] Prereq: 601.226 & linear algebra & probability. Students may receive credit for only one of 601.463/663/763. 
TuTh 4:305:45 
601.464 (E) 
ARTIFICIAL INTELLIGENCE (3) Mielke 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 34:15 
601.465 (E) 
NATURAL LANGUAGE PROCESSING (4) Eisner This course is an indepth 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 (finitestate transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, partofspeech 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. 
MWF 34:15 
601.467 (E) 
INTRODUCTION TO HUMAN LANGUAGE TECHNOLOGY (3) Koehn This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. [Applications] Prereq: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. 
TuTh 910:15 
601.468 (E) 
MACHINE TRANSLATION (3) Koehn Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems learn to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence. [Applications] Required course background: prob/stat, 601.226. Student may receive credit for at most one of 601.468/668. 
TuTh 1:302:45 
EN.601.474 (EQ) 
ML: LEARNING THEORY (3) Arora This is a graduate level course in machine learning. It will provide a formal and indepth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in largescale machine learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. [Analysis] Prereqs: 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). Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. 
TuTh 910:15a 
601.475 (E) 
MACHINE LEARNING (3) Shpitser
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 prerequisites that include probability, linear
algebra, multivariate calculus and basic optimization. Students in
the course will develop a learning system for a final project.
[Applications or Analysis] Prereqs: 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. 
MWF 1:302:45 
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] Prerequisites: familiarity with R programming and (601.475/675 or stats/probability) or permission. Students may receive credit for at most one of 601.477/677. 
TuTh 34:15 
AS.050.375 (Q) 
PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille [Was EN.601.485, now crosslisted as AS.050.375] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low, mid, and highlevel 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] Prerequisites: Calc I, programming experience (Python preferred). 
TuTh 910:15 
601.490 (E) 
INTRO TO HUMANCOMPUTER INTERACTION (3) CM Huang
This course is designed to introduce undergraduate and graduate
students to design techniques and practices in humancomputer
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 handson 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] Prereq: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. 
TuTh 34:15 
601.501 
COMPUTER SCIENCE WORKSHOP An applicationsoriented, 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 computerintegrated 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 
601.615 
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 34:15 
601.617 
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive handson experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester (odd years) and is a good introduction course to the 601.717 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses) Prereq: Intermediate Programming (C/C++) and Data Structures. Students may receive credit for only one of 601.417/617. 
TuTh 34:15 
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 1:302:45 
601.620 
PARALLEL PROGRAMMING (3) Burns Same as 601.420, for graduate students. Students may receive credit for at most one of 601.320/420/620. [Systems]
Required course background: 601.226 and 601.229 or equiv. 
MW 4:305:45 
601.621 
OBJECT ORIENTED SOFTWARE ENGINEERING Darvish Same material as 601.421, for graduate students. [Systems or Applications] (https://www.jhuoose.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. 
TuTh 1:302:45 
601.628 
COMPILERS & INTERPRETERS Hovemeyer Introduction to compiler design, including lexical analysis, parsing, syntaxdirected translation, symbol tables, runtime 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; 601.231 recommended 
MW 121:15 
601.629 
FUNCTIONAL PROGRAMMING IN SOFTWARE ENGINEERING (3) Smith How can we effectively use functional programming techniques to build realworld 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 1:302:45pm 
601.630 
COMBINATORICS AND GRAPH THEORY IN CS Li This is a graduate level course studying the applications of combinatorics and graph theory in computer science. We will start with some basic combinatorial techniques such as counting and pigeon hole principle, and then move to advanced techniques such as the probabilistic method, spectral graph theory and additive combinatorics. We shall see their applications in various areas in computer science, such as proving lower bounds in computational models, randomized algorithms, coding theory and pseudorandomness. [Analysis] Required Background: discrete math; probability theory and linear algebra recommended. Student may receive credit for only one of 601.430/601.630. 
TuTh 1:302:45p 
601.633 
INTRO ALGORITHMS Dinitz 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 121:15 
601.637 NEW COURSE 
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 handson experience with new methods and techniques. Students will gain understanding in reasoning about possible tradeoffs 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 121:15pm 
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 sameorigin policy, crossorigin resource sharing, and browser sandboxing. It will cover the most popular Web vulnerabilities, such as crosssite 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 121:15 
601.643 
SECURITY AND PRIVACY IN COMPUTING Rubin Same material as 601.443, for graduate students. [Applications] Required course background: A basic course in operating systems and networking, or permission of instructor. 
Tu 910:10, Th 910:20 
601.645

PRACTICAL CRYPTOGRAPHIC SYSTEMS Green & Chator Same material as 601.445, for graduate students. [Systems] Prereqs: data structures & computer system fundamentals. Students may receive credit for only one of 601.445/645. 
MW 121:15 
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 910:15 
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 1:302:45 
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 prerequisite. Students may receive credit for only one of 601.457/657.

MWF 1:30 
601.661 
COMPUTER VISION Leonard 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 121:15 
601.663 
ALGORITHMS FOR SENSORBASED 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. 
TuTh 4:305:45 
601.664 
ARTIFICIAL INTELLIGENCE Mielke 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 34:15p 
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. 
MWF 34:15 
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] Prereq: EN.601.226 Data Structures; knowledge of Python recommended. Students may receive credit for at most one of 601.467/667. 
TuTh 910:15 
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 1:302:45 
EN.601.674 
ML: LEARNING THEORY Arora [Formerly: Statistical Machine Learning] This is a graduate level course in machine learning. It will provide a formal and indepth coverage of topics in statistical and computational learning theory. We will revisit popular machine learning algorithms and understand their performance in terms of the size of the data (sample complexity), memory needed (space complexity), as well as the overall runtime (computational or iteration complexity). We will cover topics including PAC learning, uniform convergence, VC dimension, Rademacher complexity, algorithmic stability, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in largescale machine learning and randomized projections. General focus will be on combining methodology with theoretical and computational foundations. [Analysis] Required course background: multivariable calculus, probability, linear algebra, intro computing. Recommended: prior coursework in ML. Students may receive credit for only one of 601.474/674. 
TuTh 910:15a 
601.675 
MACHINE LEARNING Shpitser
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. 
MWF 1:302:45 
601.677 
CAUSAL INFERENCE Shpitser Same material as 601.477, for graduate students. [Analysis] Prerequisites: 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 34:15 
AS.050.675 
PROBABILISTIC MODELS OF THE VISUAL CORTEX Yuille [Was EN.601.685, now crosslisted as AS.050.675.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low, mid, and highlevel 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] Prerequisites: Calc I, programming experience (Python preferred). 
TuTh 910:15 
601.690 
INTRO TO HUMANCOMPUTER INTERACTION CM Huang
Same material as EN.601.490, for graduate students. [Applications] Prereq: basic programming skills. Students may receive credit for EN.601.490 or EN.601.690, but not both. 
TuTh 34:15 
601.714 
ADVANCED COMPUTER NETWORKS Ghorbani
This is a graduatelevel course on computer networks. It provides
a comprehensive overview on advanced topics in network protocols
and networked systems. The course will cover both classic papers
on Internet protocols and recent research results. It will examine
a wide range of topics, e.g., routing, congestion control, network
architectures, datacenter networks, network virtualization,
softwaredefined networking, and programmable networks, with an
emphasize on core networking concepts and principles. The course
will include lectures, paper discussions, programming assignments
and a research project. [Systems] Prereq: EN.601.414/614 or equivalent. 
TuTh 4:305:45pm 
601.801 
Attendance recommended for all grad students; only 1st & 2nd year PhD students may register. 
TuTh 10:3012 
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. 
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 faulttolerance, reliability, verification, energy efficiency, and virtualization. 
Fr 1:302:45 
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 1112 
601.831 
CS THEORY SEMINAR Dinitz, Li Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers. 
W 12 
601.845 
SELECTED TOPICS IN APPLIED CRYPTOGRAPHY Green In this course students will read, discuss and present current research papers in applied cryptography. Topic coverage will vary each semester. Prereq: permission of instructor. 
Tu 1212:50 
601.857  SELECTED TOPICS IN COMPUTER GRAPHICS Kazhdan In this course we will review current research in computer graphics. We will meet for an hour once a week and one of the participants will lead the discussion for the week. 
W 1:302:45 
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 121:15p 
601.866 
SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme A seminar focussed on current research and survey articles on computational semantics. 
Fr 10:4511:45 
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 11noon 
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 121:15 
500.745 
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Whitcomb, Vidal, EtienneCummings Seminar series in robotics. Topics include: Medical robotics, including computerintegrated surgical systems and imageguided 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. Humanmachine systems, including haptic and visual feedback, human perception, cognition and decision making, and humanmachine collaborative systems. Crosslisted with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering. 
Wed 121:30 
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 10  Gregory Chirikjian [MechE] 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 (was Chris CallisonBurch) 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  Ralph EtienneCummings [ECE] (was staff) 45  Liliana Florea [SOM] 46  Casey Overby Taylor [SPH] 47  Philipp Koehn 48  Abhishek Jain 49  Anton Dabhura (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  ChienMing Huang 58  Will Gray Roncal (ugrad research use 517, not 507) 59  Kevin Duh [CLSP] 60  Mihaela Pertea 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  Thomas Lippincott [CLSP] 68  Joel Bader [BME]