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 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) Mitchell 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. (www.cs.jhu.edu/~joanne/cs107) Firsttime programmers are strongly advised to take 600.108 concurrently. 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) Mitchell 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. (www.cs.jhu.edu/~joanne/cs108) CoRequisite: 600.107. 
Sec 1: Wed 69p 
600.120 (E)

INTERMEDIATE PROGRAMMING (4) Amir 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: MWF 3:004:15, CS majors only 
600.226 (E,Q) 
DATA STRUCTURES (4) Froehlich 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: MWF 121:15, CS majors only 
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) Checkoway 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. 
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 
600.318 (E) 
OPERATING SYSTEMS (4) Froehlich 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, 600.226, and 600.333. 600.211 Recommended. Students may receive credit for 600.318 or 600.418, but not both. 
MWF 10:00 
600.320 (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: 600.120 and 600.226; 600.333 recommended. Students may receive credit for 600.320 or 600.420, but not both. 
MW 4:305:45 
600.325 (E) 
DECLARATIVE METHODS (3) Eisner Suppose you could simply write down a description of your problem, and let the computer figure out how to solve it. What notation could you use? What strategy should the computer then use? In this survey class, you'll learn to recognize when your problem is an instance of satisfiability, constraint programming, logic programming, dynamic programming, or mathematical programming (e.g., integer linear programming). For each of these related paradigms, you'll learn to reformulate hard problems in the required notation and apply offtheshelf software that can solve any problem in that notation  including NPcomplete problems and many of the problems you'll see in other courses and in the real world. You'll also gain some understanding of the generalpurpose algorithms that power the software. [Analysis] Prereq: 600.226, Calc II. Students can only receive credit for 600.325 or 600.425, not both. 
MWF 3 
600.335 (E) 
ARTIFICIAL INTELLIGENCE (3) Mitchell Artificial intelligence (AI) is introduced by studying automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts. [Applications] Prereq: 600.226, 550.171; Recommended: linear algebra, prob/stat. Students can only receive credit for 600.335 or 600.435, not both. 
WF 121:15 
600.344 (E) 
COMPUTER NETWORK FUNDAMENTALS (3) DeSimone This course considers intersystem communications issues. topics covered include layered network architectures; the OSI model; bandwidth, data rates, modems, multiplexing, error detection/correction; switching; queuing models, circuit switching, packet switching; performance analysis of protocols, local area networks; and congestion control. [Systems] Prereq: 600.333 or 600.433 or permission. Students can only receive credit for 600.344 or 600.444, not both. 
TuTh 4:305:45 (was 1:302:45) 
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) 
MEDICAL 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: 1/272/19. 
MW 4:305:45 
600.411 (E) 
CS INNOVATION AND ENTREPRENEURSHIP II (1) Dabhura & 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. 
Fr 1:304:30 
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 
600.418 (E) 
OPERATING SYSTEMS (3) Froehlich Similar material as 600.318, covered in more depth. Intended for upperlevel undergraduates and graduate students. [Systems] Required course background: 600.226 and 600.233/333/433; 600.211 recommended. Students may receive credit for 600.318 or 600.418, but not both. 
MWF 10:00 
600.420 (E) 
PARALLEL PROGRAMMING (3) Burns Graduate level version of 600.320. Students may receive credit for 600.320 or 600.420, but not both. [Systems]
Required course background: 600.120 or equiv. 
MW 4:305:45 
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 (or equivalent) recommended. 
TuTh 34:15 
600.425 (E) 
DECLARATIVE METHODS (3) Eisner Graduate level version of 600.325. [Analysis] Required course background: 600.226, Calc II. Students can only receive credit for 600.325 or 600.425, not both. 
MWF 3 
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 
600.430 (HQ) 
ONTOLOGIES AND KNOWLEDGE REPRESENTATION (3) Rynasiewicz (Colisted in Philosophy: AS.150.429) Knowledge representation deals with the possible structures by which the content of what is known can be formally represented in such a way that queries can be posed and inferences drawn. Ontology concerns the hierarchical classication of entities from given domains of knowledge together with the relations between various classes, subclasses, or individuals. The main framework in which we will work is that of description logics, which are decidable fragments of varying degrees of first order predicate logic. In ontology development we will examine RDF (Resource Description Framework), its extension to RDFS, and OWL (Web Ontology Language), and use the software Protege' for specific applications. Finally, we will take a look at query languages such as SPARQL (SPARQL Protocol and RDF Query Language). [Analysis] Required course background: 600.107 and 600.271 or equivalents recommended. 
TuTh 1:302:45 
600.435 (E) 
ARTIFICIAL INTELLIGENCE (3) Mitchell Graduate level version of 600.335. [Applications] Required course background: 600.226, 550.171; linear algebra, prob/stat. Students can only receive credit for 600.335 or 600.435, not both. 
WF 121:15 
600.436 (E) 
ALGORITHMS FOR SENSORBASED ROBOTICS (3) Hager [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 
600.444 (E) 
COMPUTER NETWORKS (3) DeSimone Graduate level version of 600.344. [Systems] Required course background: 600.233/333/433 or permission. Students can only receive credit for 600.344 or 600.444, not both. 
TuTh 4:305:45 (was 1:302:45) 
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 or perm req'd. Students may receive credit for 600.446 or 600.646, but not both. 
TuTh 1:302:45 
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 
600.454 (E)

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green [Colisted with 650.445.] 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] 
MW 34:15 
600.463 (E,Q) 
ALGORITHMS I (3) Braverman Graduate version of 600.363. Students may receive credit for 600.363 or 600.463, but not both. [Analysis] Required course background: 600.226 and 550.171 or Perm. req'd. 
TuTh 9:0010:15 
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) Lopez & Post 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 
600.475 (E) 
MACHINE LEARNING (3) Dredze
This course takes an application driven approach to current topics in machine
learning. The course covers supervised learning (classification/structured
prediction/regression/ranking), unsupervised learning (dimensionality
reduction, bayesian modeling, clustering) and semisupervised learning.
Additional topics may include reinforcement learning and learning theory. The
course will also consider challenges resulting from learning applications,
such as transfer learning, multitask learning and large datasets. We will
cover popular algorithms (naive Bayes, SVM, perceptron, HMM, winnow, LDA,
kmeans, maximum entropy) and will focus on how statistical learning
algorithms are applied to real world applications. Students in the course will
implement several learning algorithms and develop a learning system for a
final project. [Applications] Required course background: multivariate calculus. 
MW 1:302:45 
600.492 (E) 
COMPUTER SCIENCE WORKSHOP II An independent 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. Permission of faculty sponsor is required. 
See below for faculty section numbers. 
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 (E) 
SENIOR THESIS IN COMPUTER INTEGRATED SURGERY (3) Prereq: 600.445 or perm req'd. 
Section 01: Taylor 
600.602 
Required for all CS PhD students. Strongly recommended for MSE students. 
TuTh 10:3012 
600.636 (E) 
ALGORITHMS FOR SENSORBASED ROBOTICS Hager [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 
600.640 
FRONTIERS OF SEQUENCING DATA ANALYSIS Langmead Public archives now contain petabytes of valuable but hardtoanalyze DNA sequencing data. Analyzing even small datasets is complicated by sequencing errors, differences between individuals, and the fragmentary nature of the the sequencing reads. In this course, we study recent algorithms and methods that seek to make sense of DNA sequencing datasets from small to very large. Topics covered will vary from year to year, but could include RNA sequencing data analysis, other functional genomics data analysis, metagenomics analysis, data compression, indexing, applications of streaming algorithms and sketch data structures, assembly, etc. There will be homework assignments and a course project. [Applications] Prereq: 600.439/639 or permission. 
MW 121:15 
600.642 
ADVANCED TOPICS IN CRYPTOGRAPHY (3) Ateniese [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. 
MW 34:15 
600.643 
ADVANCED TOPICS IN COMPUTER SECURITY Rubin [Crosslisted in ISI] Topics will vary from year to year, but will focus mainly on network perimeter protection, hostlevel protection, authentication technologies, intellectual property protection, formal analysis techniques, intrusion detection and similarly advanced subjects. Emphasis in this course is on understanding how security issues impact real systems, while maintaining an appreciation for grounding the work in fundamental science. Students will study and present various advanced research papers to the class. There will be homework assignments and a course project. [Systems or Applications] Prereq: 600.443 or 600.424; or permission of instructor. 
MW 1:302:45 
600.646 
COMPUTER INTEGRATED SURGERY II Taylor Advanced version of 600.446. [Applications] Prereq: 600.445 or perm req'd. Students may receive credit for 600.446 or 600.646, but not both. 
TuTh 1:302:45 
600.659 
INTRODUCTION TO 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. [Analysis] Required Course Background: 600.363/463. 
TuTh 121:15 
600.662 
TOPICS IN ILLUMINATION AND REFLECTANCE MODELING FOR COMPUTER VISION AND MEDICAL IMAGING APPLICATIONS Wolff The vast majority of all imagery on which computer vision is performed starts with a source of illumination in conjunction with a material reflectance property. Having a rigorous understanding of these phenomena is important for most students who want to be involved with further research in computer vision and computer integrated surgery, particularly for experimentation and development of new systems. This short course is for individuals who have already taken Computer Vision, and want to delve deeper into underlying physical modeling of light illumination, reflection, colorimetry, polarization and even sensor fusion of images taken at different wavelengths. Short Course: meets 4 weeks only. Required course background: 600.361/461 or perm req'd. 
Fr 1:304 
600.670 
PSEUDORANDOMNESS AND COMBINATORIAL CONSTRUCTIONS Li Randomness is very useful in almost all areas of computer science, such as algorithms, distributed computing and cryptography. However, computers generally do not have access to truly uniform random bits. To deal with this, we rely on various pseudorandom objects to reduce either the quantity or the quality of the random bits needed. In this course, we will develop provably good pseudorandom objects for a variety of tasks. We will frequently require explicit combinatorial constructions. That is, we will want to efficiently and deterministically construct such objects. Along the way, we will also explore the close connections of such objects to many other areas in computer science and mathematics, such as graph theory, coding theory, complexity theory and arithmetic combinatorics. [Analysis] Required Course Background: 600.271/471, 600.363/463 and probability. 
TuTh 121:15 
600.688 
FOUNDATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS II Karchin [Colisted with 580.688.] This course will introduce probabilistic modeling and information theory applied to biological sequence analysis, focusing on statistical models of protein families, alignment algorithms, and models of evolution. topics will include probability theory, score matrices, hidden Markov models, maximum likelihood, expectation maximization and dynamic programming algorithms. Homework assignments will require programming in Python. Foundations of Computational Biology I is not a prereq. [Analysis] Required course background: math through linear algebra and differential equations, at least one statistics and probability course, 580.221 or equiv., 600.226 or equiv. Undergrads may enroll by permission only. 
MW 4:305:45 
600.692 
ADVANCED TOPICS IN MACHINE LEARNING: MODELING & SEGMENTATION OF MULTIVARIATE MIXED DATA Vidal [Formerly 600.675.] 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: 600.475 or 600.476 or permission. 
TuTh 34:15 
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 Irvine, 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 1012 
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. Prereq: permission of instructor 
W 4:305:30 
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 
600.746 
SELECTED TOPICS IN 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 
600.764 
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. 
W 12 
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 
600.770 NEW COURSE 
SELECTED TOPICS IN ALGORITHMS FOR METRIC SPACES Dinitz This course will focus on algorithms that use, simplify, or exploit metric spaces. Examples of topics we will address include metric embeddings and their applications, algorithms to exploit low dimensionality, graph spanners and sparsifiers, and data structures such as distance oracles and compact routing schemes that allow us to efficiently find distances and paths. This course will mostly be in the form of a reading group, and students will present a paper and lead a discussion. Prereq: 600.463 or permission of the instructor. 
Mon 11 
600.771 
PROBABILITY ON STRINGS, TREES, AND SEQUENCES Lopez Many areas of practical computer science focus on discrete data that is sequential or treeshaped: natural language processing (sentences and their analyses), computational biology (DNA and protein structures), programming languages (computer programs and their interpretations), and compression (sequences of bits). When the data is noisy or ambiguous, decisionmaking requires probabilistic methods. We will survey formal tools for manipulating sets of strings, trees, sequences, and defining probabilistic models over these sets. Much of the material covers advanced topics at the intersection of formal language and automata theory, probability, and algorithms. Respectively, these three areas will enable us to represent sets, represent uncertainty, and process everything efficiently. 
Th 4:30 
600.775 
SELECTED TOPICS IN MACHINE LEARNING (was DATA INTENSIVE COMPUTING & MACHINE LEARNING SEMINAR) Dredze, 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. 
Mon 121:15 
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. 
600.810 
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  Larry Wolff 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