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 4/28/20 for most undergraduate courses and 8/1/2020 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 are changed effective July 2019. Previously there were 3 designations  Analysis, Systems, Applications  and these still appear in the course descriptions below for grandfathering purposes. Going forward 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  note that some suffixes were changed as noted in bold. 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.]
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  3p. Sections 6 & 7 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 9/2 
601.220 (E)

INTERMEDIATE PROGRAMMING (4) Amir/Darvish/Selinski 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 601/600.107, 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 600.107/601.107, 600.120/601.220, 500.112, 500.113+500.132, 500.114+500.132 or equivalent by permission. 
Sec 01: MWF 121:15, 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: 600.120/601.220. 
01: MWF 9, limit 75 
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 601.231/600.271 and 601.631/600.471 for the same degree. Prereq: 550/553.171/172. 
TuTh 910:15, limit 90 
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. 
01: TuTh 121:15p, limit 19 
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: 600/601.226. Students may receive credit for only one of 601.315/415/615. 
01: TuTh 34:15,
limit 30 
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: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618. 
TuTh 1:302:45 
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 4:305:45 
601.329 (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 1:302:45 
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: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640. 
TuTh 121:15 
601.414 (E) 
COMPUTER NETWORKS (3) Jin 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 handson programming assignments, homeworks and two exams. [Systems] Prerequisites: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
TuTh 1:302:45 
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: 600.226/601.226. Students may receive credit for only one of 601.315/415/615. 
TuTh 34:15 
601.418 (E) 
OPERATING SYSTEMS (3) Huang Similar material as 600.318, covered in more depth, for advanced undergraduates. [Systems] Prereq: 600.120/601.220 & 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.318/418/618. 
TuTh 1:302:45 
601.419 (E) 
CLOUD COMPUTING (3) Ghorbani
Clouds host a wide range of applications that we rely on today. In
this course, we study foundational cloud systems and
infrastructures, common cloud applications and the traffic
patterns that they generate, and core networking and distributed
systems concepts and algorithms that enable cloud computing. We
will also study how today's application demand is influencing the
network design, explore current practice, and how we can build
future networked infrastructure to better enable both efficient
transfers of big data and lowlatency requirements of realtime
applications. The format of this course will be a mix of lectures,
readings, discussions, assignments and reviews, a final exam, and
a project. [Systems]
Prerequisites: EN.601.226 and
EN.601.414, or permission. Students
can only receive credit for one of 601.419/619. 
TuTh 34:15 
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 4:305:45 
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. Students may receive credit for only one of 601.421/621. 
Lec: Tu 1:302:45 
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: 600.120/601.220 and 600.226/601.226 and 600.233/601.229; 600.271/601.231 recommended 
MW 121:15 
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: 601/600.226 & (550/553.171/172 or 601.231/600.271) or Perm. Required. Students may receive credit for only one of 601.433/633. 
TuTh 121:15 
601.434 (EQ) 
RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, derandomization, existence proofs, universal hashing, markov chains, metropolis and metropolishastings methods, mixing by coupling and by eigenvalues, counting problems, semidefinite programming and rounding, lower bound arguments, and applications of expanders. [Analysis] Prereq: 600.363/463/601.433/633 and (550.310/553.310/553.311 or 550.420/620 or equivalent). Students may receive credit for only one of 601.434/634. 
TuTh 121:15

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: 600/601.226 & 600.233/601.229. Students may receive credit for only one of 601.340/440/640. 
TuTh 121:15 
601.441 (E) 
BLOCKCHAINS AND CRYPTOCURRENCIES Green This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641. Prereq: 601.226 and (EN.553.310 or EN.553.410 probability). 
MW 121:15 
601.442 (EQ) 
MODERN CRYPTOGRAPHY (3) Jain Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zeroknowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis] Prerequisite: 601.231/600.271/471 & (550.310/553.310 or 553.331 or 550.420/553.420). Students may receive credit for only one of 601.442/642. 
MW 1:302:45 
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: 600.233/601.229 & (600/601.318/418 or 600.444/601.414) Students may receive credit for only one of 601.443/643. 
Tu 910:10, Th 910:20 
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: 600.120/601.220 & 600/601.226. Students may receive credit for at most one of 601.447/647/747. 
TuTh 910:15 
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: 600/601.226 and linear algebra, or permission. Recmd: 600.120/601.220, 600/601.457, 600/601.461, image processing. Students may earn credit for only one of 601.455/655. 
TuTh 1:302:45 
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; 600.120/601.220 & 600/601.226 & 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) Hager This course provides an overview of fundamental methods in computer vision from a computational perspective. Methods studied include: camera systems and their modelling, computation of 3D geometry from binocular stereo, motion, and photometric stereo, and object recognition, image segmentation, and activity analysis. Elements of machine learning and deep learning 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: 600.226/601.226 & linear algebra & probability. Students may receive credit for only one of 600.336/436/636 and 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 
Sec 02: TuTh 10:3011:45 
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: 600/601.226. 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 (was MW 34:15) 
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, 600/601.226. Student may receive credit for at most one of 601.468/668. 
TuTh 1:302:45 
601.475 (E) 
MACHINE LEARNING (3) Dredze/WoodDoughty
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 (550.310/553.310/553.311 or 550.420/553.420 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.601.220/600.120, AS.250.205, EN.580.200, EN.600/601.107). Students may receive credit for only one of 601.475/675. 
01 (limit 45): MW 1:302:45, sect F 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 (600/601.475/675 or stats/probability) or permission. Students may receive credit for at most one of 601.477/677. 
TuTh 34:15 
601.482 (E) 
MACHINE LEARNING: DEEP LEARNING (4) Unberath
Deep learning (DL) has emerged as a powerful tool for solving
dataintensive 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 perceptionbased
robotics. Prereq: (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); Calc III and numerical optimization recommended. Recommended coreq: EN.601.382. 
MWF 121:15p 
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 [Formerly 600.491] 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: 600.445 or perm req'd. 
Section 1: Taylor 
601.614 
Same as 601.414, for graduate students. [Systems] Required course background: EN.601.220 and EN.601.229 or permission. Students can only receive credit for one of 601.414/614. 
TuTh 1:302:45 
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 
601.618 
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 
601.619 
CLOUD COMPUTING Ghorbani [Same as 601.419, for graduate students.] Clouds host a wide range of applications that we rely on today. In this course, we study foundational cloud systems and infrastructures, common cloud applications and the traffic patterns that they generate, and core networking and distributed systems concepts and algorithms that enable cloud computing. We will also study how today's application demand is influencing the network design, explore current practice, and how we can build future networked infrastructure to better enable both efficient transfers of big data and lowlatency requirements of realtime applications. The format of this course will be a mix of lectures, readings, discussions, assignments and reviews, a final exam, and a project. [Systems] Required course background: EN.601.226 Data Structures and EN.601.414/614 Computer Networks. Students can only receive credit for one of 601.419/619. 
TuTh 34:15 
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. Students may receive credit for only one of 601.421/621. 
Lec: Tu 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: 600.120/601.220 and 600.226/601.226 and 600.233/601.229; 600.271/601.231 recommended 
MW 121:15 
601.631 
THEORY OF COMPUTATION (3) Li This is a graduatelevel course studying the theoretical foundations of computer science. Topics covered 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 601.631, unless one is for an undergrad degree and the other for grad. [Analysis] Prereq: discrete math or permission. 
TuTh 121:15 
601.633 
INTRO ALGORITHMS Dinitz Same material as 600.433, 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 
601.634 
RANDOMIZED & BIG DATA ALGORITHMS Braverman Same material as 601.434, for graduate students. [Analysis] (www.cs.jhu.edu/~cs464) Required Background: Algorithms and probability. Students may receive credit for only one of 601.434/634. 
TuTh 121:15

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 
601.641 
BLOCKCHAINS AND CRYPTOCURRENCIES Green [Crosslisted in JHUISI; Same as EN.601.441, for graduate students.] This course will introduce students to cryptocurrencies and the main underlying technology of Blockchains. The course will start with the relevant background in cryptography and then proceed to cover the recent advances in the design and applications of blockchains. This course should primarily appeal to students who want to conduct research in this area or wish to build new applications on top of blockchains. It should also appeal to those who have a casual interest in this topic or are generally interested in cryptography. Students are expected to have mathematical maturity. [Analysis] Students may receive credit for only one of 600.451, 601.441, 601.641. Required course background: 601.226 and probability (any course). 
MW 121:15 
601.642 
MODERN CRYPTOGRAPHYJain Same material as 601.442, for graduate students. [Analysis] Required course background: Probability & Automata/Computation Theory. 
MW 1:302:45 
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 
601.647 
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 Hager 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 600.336/436/636 or 601.463/663/763. 
TuTh 4:305:45 
601.665 
NATURAL LANGUAGE PROCESSING Eisner Same material as 601.465, for graduate students. [Applications] (www.cs.jhu.edu/~jason/465) Prerequisite: data structures. 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 (was MW 34:15) 
601.668 
MACHINE TRANSLATION Koehn Same material as 601.468, for graduate students. [Applications] Required course background: prob/stat, 600.226. Student may receive credit for at most one of 601.468/668. 
TuTh 1:302:45 
601.675 
MACHINE LEARNING Dredze/WoodDoughty
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. 
01 (limit 65): MW 1:302:45, sect F 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 
601.682 
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. Recommended coreq: EN.601.382. 
MWF 121:15p 
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 
601.780 CSCIREAS 
UNSUPERVISED LEARNING: FROM BIG DATA TO LOWDIMENSIONAL REPRESENTATIONS Vidal (Previously ADVANCED TOPICS IN MACHINE LEARNING: MODELING & SEGMENTATION OF MULTIVARIATE MIXED DATA.) In the era of data deluge, the development of methods for discovering structure in highdimensional data is becoming increasingly important. This course will cover stateoftheart methods from algebraic geometry, sparse and lowrank representations, and statistical learning for modeling and clustering highdimensional data. The first part of the course will cover methods for modeling data with a single lowdimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and lowrank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging. [Applications] Required course background: linear algebra, optimization and statistics. Prior exposure to machine learning (e.g., 601.475/675) is a plus. 
TuFr 34:15p 
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 
601.814 
SELECTED TOPICS IN COMPUTER NETWORKS Jin In this course we will read, discuss and present classic papers and current research in computer networks. The topic coverage will vary each semester. 
W 45 
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 12:15 
601.819 
SELECTED TOPICS IN CLOUD COMPUTING AND NETWORKED SYSTEMS Ghorbani Participants will read and discuss seminal and recent foundational research on cloud and networked systems. 
Fr 45p 
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. 
Th 12 
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. 
W 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  staff [Joao Sedoc] 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  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  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]