See the calendar layout for a convenient listing of course times and room requests.
Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in January. Changes to the original posted schedule are noted in red.
600.104 (H) 
COMPUTER ETHICS (1) Sheela Kosaraju 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. 
We 68p, alternate weeks (start tba) 
600.107 (E) 
INTRO TO PROGRAMMING IN JAVA (3) More This course introduces fundamental structured and objectoriented programming concepts and techniques, using Java, and is intended for all who plan to use computer programming in their studies and careers. Topics covered include variables, arithmetic operators, control structures, arrays, functions, recursion, dynamic memory allocation, files, class usage and class writing. Program design and testing are also covered, in addition to more advanced objectoriented concepts including inheritance and exceptions as time permits. Firsttime programmers are strongly advised to take 600.108 concurrently in Fall/Spring semesters. Prereq: familiarity with computers. Students may receive credit for 600.107 or 600.112, but not both. 
MW 1:302:45 
600.108 (E) 
INTRO PROGRAMMING LAB (1) More Satisfactory/Unsatisfactory only. This course is intended for novice programmers, and must be taken in conjunction with 600.107. The purpose of this course is to give firsttime programmers extra handson practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Prerequisite: familiarity with computers. CoRequisite: 600.107. 
Sec 1: Wed 69p, limit 24 
600.120 (E)

INTERMEDIATE PROGRAMMING (4) Langmead/More 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, 600.107, 600.111, 600.112 or equivalent. 
Sec 01 (More): MWF 121:15, limit 30 
600.226 (E,Q) 
DATA STRUCTURES (4) Hager/Selinski This course covers the design and implementation of data structures including collections, sequences, trees, and graphs. Other topics include sorting, searching, and hashing. Course work involves both written homework and Java programming assignments. Prereq: AP CS, 600.107 or 600.120. 
Sec 01 (Hager): MWF 1:302:45, CS/CE majors/minors only, limit 45 
600.233 (E) 
COMPUTER SYSTEM FUNDAMENTALS (3) Froehlich [Formerly 600.333/433] 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. [Systems] Prereq: 600.120. Students may receive credit for only one of 600.233, 600.333 or 600.433. 
MWF 1:30 
600.250 (E) 
USER INTERFACES AND MOBILE APPLICATIONS (3) Selinski This course will provide students with a rich development experience, focused on the design and implementation of user interfaces and mobile applications. A brief overview of human computer interaction will provide context for designing, prototyping and evaluating user interfaces. Students will invent their own mobile applications and implement them using the Android SDK, which is JAVA based. An overview of the Android platform and available technologies will be provided, as well as XML for layouts, and general concepts for effective mobile development. Students will be expected to explore and experiment with outside resources in order to learn technical details independently. There will also be an emphasis on building teamwork skills, and on using modern development techniques and tools. Prereq: 600.120 and 600.226. 
TuTh 34:15 
600.271 (E,Q) 
AUTOMATA and COMPUTATION THEORY (3) Li 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 600.271 and 600.471 for the same degree. Prereq: 550.171. 
TuTh 1:302:45 
600.316 (E) 
DATABASE SYSTEMS (3) Ahmad This course serves as an introduction to the architecture and design of modern database management systems. topics include query processing algorithms and data structures, data organization and storage, query optimization and cost modeling, transaction management and concurrency control, highavailability mechanisms, parallel and distributed databases, and a survey of modern architectures including NoSQL, columnoriented and streaming databases. Course work includes programming assignments and experimentation in a simple database framework written in Java. [Systems] Prereq: 600.120 and 600.226. Students may receive credit for 600.316 or 600.416, but not both. 
MW 121:15 
600.328 (E) 
COMPILERS & INTERPRETERS (3) Froehlich 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 & 600.226 
MWF 10 
600.340 (E) 
INTRODUCTION TO GENOMIC RESEARCH (3) Salzberg This course will use a projectbased approach to introduce undergraduates to research in computational biology and genomics. During the semester, students will take a series of large data sets, all derived from recent research, and learn all the computational steps required to convert raw data into a polished analysis. Data challenges might include the DNA sequences from a bacterial genome project, the RNA sequences from an experiment to measure gene expression, the DNA from a human microbiome sequencing experiment, and others. Topics may vary from year to year. In addition to computational data analysis, students will learn to do critical reading of the scientific literature by reading highprofile research papers that generated groundbreaking or controversial results. [Applications] Prerequisites: knowledge of the Unix operating system and programming expertise in a language such as Perl or Python.

TuTh 34:15 
600.344 (E) CANCELED  take 600.444 
COMPUTER NETWORK FUNDAMENTALS (3) Rubin 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] Prereq: EN.600.120 and EN.600.233 or permission. Students can only receive credit for 600.344 or 600.444, not both. 
CANCELED (take 600.444) 
600.355 (E) 
VIDEO GAME DESIGN PROJECT (3) Froehlich An intensive capstone design project experience in video game development. Students will work in groups of 48 on developing a complete video game of publishable quality. Teams will (hopefully) include programmers, visual artists, composers, and writers. Students will be mentored by experts from industry and academia. Aside from the project itself, project management and communication skills will be emphasized. Enrollment is limited to ensure parity between the various disciplines. [General] May involve travel to MICA. Prereq: 600.255/256 or permission of instructor; junior or senior standing recommended. 
Wed 4:307:30p 
600.363 (E,Q) 
INTRODUCTION TO ALGORITHMS (3) Braverman 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: 600.226 and 550.171 or Perm. Req'd. Students may receive credit for 600.363 or 600.463, but not both. 
TuTh 910:15 
600.402 (E) 
DIGITAL HEALTH AND BIOMEDICAL INFORMATICS (1) Lehmann Advances in technology are driving a change in medicine, from personalized medicine to population health. Computers and information technology will be critical to this transition. We shall discuss some of the coming changes in terms of computer technology, including computerbased patient records, clinical practice guidelines, and regionwide health information exchanges. We will discuss the underlying technologies driving these developments  databases and warehouses, controlled vocabularies, and decision support. Prerequisite: none. Short course meets 4 weeks: 2/1  2/24. 
MW 4:305:45 
600.411 (E) 
CS INNOVATION AND ENTREPRENEURSHIP II (3) Dahbura & Aronhime This course is the second half of a twocourse sequence and is a continuation of course 660.410.01, CS Innovation and Entrepreneurship, offered by the Center for Leadership Education (CLE). In this sequel course the student groups, directed by CS faculty, will implement the business idea which was developed in the first course and will present the implementations and business plans to an outside panel made up of practitioners, industry representatives, and venture capitalists. [General] Prerequisites: 660.410 and 600.321/421. 
Tu 58p (was M 35:45) 
600.416 (E) 
DATABASE SYSTEMS (3) Ahmad Similar material as 600.316, covered in more depth. Intended for upperlevel undergraduates and graduate students. [Systems] Required course background: 600.120 and 600.226. Students may receive credit for 600.316 or 600.416, but not both. 
MW 121:15 
600.424 (E) 
NETWORK SECURITY (3) Nielson [Crosslisted in ISI] This course focuses on communication security in computer systems and networks. The course is intended to provide students with an introduction to the field of network security. The course covers network security services such as authentication and access control, integrity and confidentiality of data, firewalls and related technologies, Web security and privacy. Course work involves implementing various security techniques. A course project is required. [Systems] Required course background: 600.120, 600.226, 600.344/444 or permission. 
TuTh 34:15 
600.426 (E,Q) 
PRINCIPLES OF PROGRAMMING LANGUAGES (3) Smith Functional, objectoriented, and other language features are studied independent of a particular programming language. Students become familiar with these features by implementing them. Most of the implementations are in the form of small language interpreters. Some type checkers and a small compiler will also be written. The total amount of code written will not be overly large, as the emphasis is on concepts. The ML programming language is the implementation language used. [Analysis] Required course background: 600.226. Freshmen and sophomores by permission only. 
MW 1:302:45 
600.428 (E) 
COMPILERS & INTERPRETERS (3) Froehlich Graduate level version of 600.328. Students may receive credit for 600.328 or 600.428, but not both. [Systems] Prereq: 600.120 & 600.226 
MWF 10 
600.436 (E) 
ALGORITHMS FOR SENSORBASED ROBOTICS (3) Leonard [Formerly 600.336.] 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, linear algebra, probability. Students may receive credit for only one of 600.336, 600.436 and 600.636. 
TuTh 121:15 
600.438 (E) (was 338) NEW COURSE! 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle Genomic data has the potential to reveal causes of disease, novel drug targets, and relationships among genes and pathways in our cells. However, identifying meaningful patterns from highdimensional genomic data has required development of new computational tools. This course will cover current approaches in computational analysis of genomic data with a focus on statistical methods and machine learning. Topics will include disease association, prediction tasks, clustering and dimensionality reduction, data integration, and network reconstruction. There will be some programming and a project component. [Applications] Recommended Course Background: 600.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for 600.338 or 600.638, but not both. 
TuTh 9 
600.444 (E) 
COMPUTER NETWORKS (3) Rubin Graduate level version of 600.344. [Systems] Required course background: EN.600.120 and EN.600.233/433 or permission. Students can only receive credit for 600.344 or 600.444, not both. 
TuTh 910:15 
600.446 (E) 
COMPUTER INTEGRATED SURGERY II (3) Taylor This weekly lecture/seminar course addresses similar material to 600.445, but covers selected topics in greater depth. In addition to material covered in lectures/seminars by the instructor and other faculty, students are expected to read and provide critical analysis/presentations of selected papers in recitation sessions. Students taking this course are required to undertake and report on a significant term project under the supervision of the instructor and clinical end users. Typically, this project is an extension of the term project from 600.445, although it does not have to be. Grades are based both on the project and on classroom recitations. Students wishing to attend the weekly lectures as a 1credit seminar should sign up for 600.452. Students may also take this course as 600.646. The only difference between 600.446 and 600.646 is the level of project undertaken. Typically, 600.646 projects require a greater degree of mathematical, image processing, or modeling background. Prospective students should consult with the instructor as to which course number is appropriate. [Applications]
Prereq: 600.445/645 or perm req'd. Students may receive credit for
600.446 or 600.646, but not both. 
TuTh 1:302:45 
600.451 (E) CANCELLED 
INTRO TO BITCOIN AND OTHER CRYPTOCURRENCIES (3) Ateniese This course covers the basics of Bitcoin and the underlying technologies driving it. The course is intended for students interested in the cryptographic techniques devised to make digital currencies and payment systems secure. Topics include Bitcoin transactions, the blockchain, mining, and decentralized consensus. The course will include a brief introduction to publickey cryptography, digital signatures, hash functions, proof of work/space, multisignatures, and elliptic curve cryptography. The course concludes with an overview of the Bitcoin scripting language and Bitcoin 2.0 platforms. [Systems] Prereq: 600.226 required; 600.344/444 and 550.171 recommended. 
CANCELLED 
600.452 (E) 
COMPUTER INTEGRATED SURGERY SEMINAR (1) Taylor Lecture only version of 600.446 (no project). Prereq: 600.445 or perm req'd. Students may receive credit for 600.446 or 600.452, but not both. 
TuTh 1:302:45 
600.459 (E,Q) 
COMPUTATIONAL GEOMETRY Kazhdan This course will provide an introduction to computational geometry. It will cover a number of topics in two and threedimensions, including polygon triangulations and partitions, convex hulls, Delaunay and Voronoi diagrams, arrangements, and spatial queries. Timepermitting, we will also look at kDtrees, general BSPtrees, and quadtrees. [Analysis] Prereq: 600.120, 600.226, 600.363/463. Students may receive credit for 600.459 or 600.659, but not both. 
MW 1:302:45 
600.463 (E,Q) 
ALGORITHMS I (3) Braverman Graduate version of 600.363. [Analysis] Students may receive credit for 600.363 or 600.463, but not both. Required course background: 600.226 and 550.171 or Perm. req'd. 
TuTh 9:0010:15 
600.465 (E) 
NATURAL LANGUAGE PROCESSING (3) 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: 600.226. 
MWF 34:15 
600.466 (E) 
INFORMATION RETRIEVAL & WEB AGENTS (3) Yarowsky An indepth, handson study of current information retrieval techniques and their application to developing intelligent WWW agents. Topics include a comprehensive study of current document retrieval models, mail/news routing and filtering, document clustering, automatic indexing, query expansion, relevance feedback, user modeling, information visualization and usage pattern analysis. In addition, the course explores the range of additional language processing steps useful for template filling and information extraction from retrieved documents, focusing on recent, primarily statistical methods. The course concludes with a study of current issues in information retrieval and data mining on the World Wide Web. Topics include web robots, spiders, agents and search engines, exploring both their practical implementation and the economic and legal issues surrounding their use. [Applications] Required course background: 600.226. 
TuTh 34:15 
600.468 (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, 600.226, 600.465. 
TuTh 1:302:45 
600.473 (E,Q) 
ALGORITHMIC GAME THEORY Dinitz This course provides an introduction to algorithmic game theory: the study of games from the perspective of algorithms and theoretical computer science. There will be a particular focus on games that arise naturally from economic interactions involving computer systems (such as economic interactions between largescale networks, online advertising markets, etc.), but there will also be broad coverage of games and mechanisms of all sorts. Topics covered will include a) complexity of computing equilibria and algorithms for doing so, b) (in)efficiency of equilibria, and c) algorithmic mechanism design. Students may receive credit for 600.473 or 600.673, but not both. Prereq: 600.363/463 or permission. [Analysis] Prereq: 600.363/463 or permission. 
TuTh 34:15 
600.476 (EQ) 
MACHINE LEARNING: DATA TO MODELS (3) Saria [Formerly "Machine Learning in Complex Domains"] How can robots localize themselves in an environment when navigating? Can we predict which patients are at greatestrisk for complications in the hospital? Which movie should I recommend to this user given his history of likes? Many such big data questions can be answered using the paradigm of probabilistic models in machine learning. These are especially useful when common offtheshelf algorithms such as support vector machines and kmeans fail. You will learn methods for clustering, classification, structured prediction, recommendation and inference. We will use Murphy's book, Machine Learning: a Probabilistic Perspective, as the text for this course. Assignments are solved in groups of size 13 students. The class will have 4 interactive sessions during which we brainstorm how to solve example openended realworld problems with the tools learnt in class. Students are also required to do a project of their choice within which they experiment with the ideas learnt in class. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both. Prereqs: 1) Intro Prob/Stat, Linear Algebra and Intro Machine Learning OR 2) Strong background in statistics (at least 12 upper level classes in statistics) and programming (fluency with ideally Python and in the very least R/Matlab). 
TuTh 4:305:45 
600.484 (E) NEW COURSE ADDED! 
AUGMENTED REALITY (3) Navab
Undergraduate level version of 600.684.
[Applications] Students may receive credit for 600.384 or 600.684, but
not both. Prerequisites: EN.600.120, EN.600.226, linear algebra. 
TuTh 910:15 
600.504 
UNDERGRADUATE INDEPENDENT STUDY Individual guided study for undergraduates, 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 of faculty sponsor is required.

See below for faculty section numbers. 
600.508 
UNDERGRADUATE RESEARCH Permission of faculty sponsor is required. 
See below for faculty section numbers. 
600.510 
COMPUTER SCIENCE INTERNSHIP Individual work in the field with a learning component, supervised by a faculty member in the department. The program of study 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, which is the limit per semester. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
600.520 
SENIOR HONORS THESIS (3) For computer science majors only, a continuation of 600.519. Prerequisite: 600.519 
See below for faculty section numbers. 
600.546 
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) Prereq: 600.445 or perm req'd. 
Section 01: Taylor 
600.592 
COMPUTER SCIENCE WORKSHOP II [Previously numbered 492] 
See below for faculty section numbers. 
600.602 
Required for all CS PhD students. Strongly recommended for MSE students. 
TuTh 10:3012 
600.636 
ALGORITHMS FOR SENSORBASED ROBOTICS Leonard [Formerly 600.436.] Graduate level version of 600.436 (see description above). [Analysis] Required course background: 600.226, calculus, prob/stat. Students may receive credit for only one of 600.336, 600.436 or 600.636. 
TuTh 121:15 
600.638 NEW COURSE! 
COMPUTATIONAL GENOMICS: DATA ANALYSIS (3) Battle Graduate version of 600.338. Students may receive credit for 600.338 or 600.638, but not both. [Applications] Recommended Course Background: 600.226 or other programming experience, probability and statistics, linear algebra or calculus. Students may receive credit for 600.338 or 600.638, but not both. 
TuTh 9 
600.642 
ADVANCED TOPICS IN CRYPTOGRAPHY (3) Jain [Crosslisted in ISI] This course will focus on advanced cryptographic topics with an emphasis on open research problems and student presentations. [Analysis] Prereq: 600.442 or 600.472 or permission. 
F 1:304 
600.646 
COMPUTER INTEGRATED SURGERY II Taylor Advanced version of 600.446. [Applications] Prereq: 600.445/645 or perm req'd. Students may receive credit for 600.446 or 600.646, but not both. 
TuTh 1:302:45 
600.673 
ALGORITHMIC GAME THEORY Dinitz Take 600.473 instead  we will only offer one version of the course for both undergrads and grads. Prereq: 600.363/463 or permission. 
TuTh 34:15 
600.675

STATISTICAL MACHINE LEARNING Arora This is a second graduate level course in machine learning. It will provide a formal and an indepth coverage of topics at the interface of statistical theory and computational sciences. 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 computational runtime (computation or iteration complexity). We will cover topics including nonparametric methods, kernel methods, online learning and reinforcement learning, as well as introduce students to current topics in largescale machinelearning and randomized projections. Topics will vary from yeartoyear but the general focus would be on combining methodology with theoretical and computational foundations. [Analysis or Applications] Prereq: 600.475 or 600.476/676 or permission. 
MWF 1:302:45 
600.676 
MACHINE LEARNING: DATA TO MODELS Saria [Formerly "Machine Learning in Complex Domains"] Graduate version of 600.476. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both. Prereqs: 1) Intro Prob/Stat, Linear Algebra and Intro Machine Learning OR 2) Strong background in statistics (at least 12 upper level classes in statistics) and programming (fluency with ideally Python and in the very least R/Matlab). 
TuTh 4:305:45 
600.682 NEW COURSE ADDED! 
DEEP LEARNING FOR IMAGE UNDERSTANDING Lu This course discusses advanced topics on the recent progresses using deep learning, specifically deep convolutional neural networks in computer vision and medical image analysis.Topics will be selected from most recent papers from CVPR/ICCV/ArXiv/NIPS/MICCAI, with the core focus on object/scene recognition, object detection, domain transfer learning and computeraided diagnosis. This course is targeted toward graduate students who are interested in mastering the understanding of the recent massive amount of literature and applying the skills to a course project (with lectures, paper reading, inclass presentation & discussion and a final research project). [Applications] Prereqs: EN.600.461/661 or permission. 
Mon 10:3011:45, 1:302:45 
600.684 ADDED! 
AUGMENTED REALITY (3) Navab
This course introduces students to the field of Augmented
Reality. It reviews its basic definitions, principles and
applications. It then focuses on Medical Augmented Reality and its
particular requirements. The course also discusses the main issues of
calibration, tracking, multimodal registration, advance visualization
and display technologies. Homework in this course will relate to the
mathematical methods used for calibration, tracking and visualization
in medical augmented reality. Students may also be asked to read
papers and implement various techniques within group
projects. [Applications] Students may receive credit for 600.484 or 600.684, but
not both. Required course background: intermediate programming (C/C++), data structures, linear algebra. 
TuTh 910:15 
600.692 
ADVANCED TOPICS IN MACHINE LEARNING: MODELING & SEGMENTATION OF MULTIVARIATE MIXED DATA Vidal 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., 600.475) is a plus. 
MW 121:15 
520.702 
CURRENT TOPICS IN LANGUAGE AND SPEECH PROCESSING Khudanpur CLSP seminar series, for any students interested in current topics in language and speech processing. 
Tu & Fr 121:15 
600.707 
SELECTED TOPICS IN CS EDUCATION Selinski This course will explore current issues and research in computer science education. Topics will be drawn from literature, news items, and participant experience. Current faculty and students with interests in academic careers are encouraged to attend. 
tbd 
600.716 
SELECTED TOPICS ON INNOVATIVE DATA SYSTEMS Ahmad This weekly reading group will survey and dissect the cuttingedge on innovative data systems research. Topics will encompass methods and abstraction in core systems and data management areas (e.g., cloud computing, scalable programming and storage), as well as usecases and "war" stories from industry, and science and engineering applications. Our semester schedule is posted at damsel.cs.jhu.edu/blockparty. 
Th 1:302:30 
600.726 
SELECTED TOPICS IN PROGRAMMING LANGUAGES Smith This seminar course covers recent developments in the foundations of programming language design and implementation. topics covered include type theory, process algebra, higherorder program analysis, and constraint systems. Students will be expected to present papers orally. 
Wed 1011 
600.728 
SELECTED TOPICS IN CATEGORY THEORY Filardo Students in this course will read a sampling of standard texts in Category Theory (e.g. the books by Awodey, Mac Lane, Pierce, or others) and papers relevant to the research of participants. 
TBA

500.745 
SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Cowan, 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 
600.746 
SEMINAR: MEDICAL IMAGE ANALYSIS Taylor & Prince This weekly seminar will focus on research issues in medical image analysis, including image segmentation, registration, statistical modeling, and applications. It will also include selected topics relating to medical image acquisition, especially where they relate to analysis. The purpose of the course is to provide the participants with a thorough background in current research in these areas, as well as to promote greater awareness and interaction between multiple research groups within the University. The format of the course is informal. Students will read selected papers. All students will be assumed to have read these papers by the time the paper is scheduled for discussion. But individual students will be assigned on a rotating basis to lead the discussion on particular papers or sections of papers. Colisted with 520.746. 
Tu 34:50 
600.760 New! 
CS THEORY SEMINAR Braverman, 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 
600.764 
SEMINAR IN ALGORITHMS Braverman This course will explore algorithms and theoretical computer science with a focus on algorithms for massive data. Examples of topics include streaming algorithms, approximation algorithms, online algorithms. Students will be encouraged to select a paper and lead a discussion. External speakers will be invited to present current work as well. This course is a good opportunity for motivated students to learn modern algorithmic methods. Prereq: 600.463 or equivalent. 
Th 121 
600.765 
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. Required course background: 600.465 or permission of instructor. 
Th 121:15 
600.766 
SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme A seminar focussed on current research and survey articles on computational semantics. 
Fr 1010:50 
600.767 NEW COURSE ADDED! 
SELECTED TOPICS IN SYSTEMS RESEARCH Amir Students will review, present, and discuss current research in computer systems, distributed systems, and computer networks, in the contexts of dependability, performance and scalability. 
Th 1:302:45 
600.768 New! 
SELECTED TOPICS IN MACHINE TRANSLATION Koehn Students in this course will review, present, and discuss current research in machine translation. Prereq: permission of instructor. 
T 9:3010:30 
600.775 
SELECTED TOPICS IN MACHINE LEARNING Saria, Arora This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the stateoftheart across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from timetotime. Required course background: a machine learning course or permission of instructor. 
Thu 34:15 
600.780 CANCELLED 
SELECTED TOPICS IN COMPUTATIONAL GENOMICS Langmead This course will survey current areas where computer science approaches have been applied to genomics research. Chiefly, the course focuses on DNA sequencing data analysis, including sequence alignment, de novo assembly, error correction, and DNA data compression. Subject matter will be partially guided by student interests. Students will present papers orally. 
M 11 
600.802 
DISSERTATION RESEARCH 
See below for faculty section numbers. 
600.804 
GRADUATE RESEARCH Independent research for masters or predissertation PhD students. Permission required. 
See below for faculty section numbers. 
600.807 ADDED! 
TEACHING PRACTICUM Selinski 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 
600.810 
GRADUATE INDEPENDENT STUDY Permission Required. 
See below for faculty section numbers. 
01  Xin Li 02  Rao Kosaraju 03  Yanif Ahmad 04  Russ Taylor 05  Scott Smith 06  Joanne Selinski 07  Harold Lehmann 08  John Sheppard 09  Greg Hager 10  Greg Chirikjian 11  Sanjeev Khudhanpur 12  Yair Amir 13  David Yarowsky 14  Noah Cowan 15  Randal Burns 16  Jason Eisner 17  Mark Dredze 18  Michael Dinitz 19  Rachel Karchin 20  Guiseppe Ateniese 21  Avi Rubin 22  Matt Green 23  Andreas Terzis 24  Raman Arora 25  Rai Winslow 26  Misha Kazhdan 27  Chris CallisonBurch 28  Peter Froehlich 29  Alex Szalay 30  Peter Kazanzides 31  Jerry Prince 32  Rajesh Kumar 33  Nassir Navab 34  Rene Vidal 35  Alexis Battle 36  Emad Boctor 37  Joel Bader 38  Ben VanDurme 39  Jeff Siewerdsen 40  Vladimir Braverman 41  Suchi Saria 42  Ben Langmead 43  Steven Salzberg 44  Stephen Checkoway 45  Liliana Florea 46  Adam Lopez 47  Philipp Koehn 48  Abhishek Jain 49  Anton Dabhura 50  Joshua Vogelstein 51  Ilya Shpitser 52  Austin Reiter 53  Tamas Budavari