Courses without end times are assumed to meet for 50 minute periods. Final room assignments will be available on the Registrar's website in September. 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. 
Sec 01: Wed 68p, alternate weeks 
600.105 
M&Ms: FRESHMAN EXPERIENCE (1) Selinski This course is required for all freshmen Computer Science majors. Transfers into the major and minors may enroll by permission only. Students will attend four 3week blocks of meetings with different computer science professors, focused on a central theme. Active participation is required. Satisfactory/Unsatisfactory only. 
Tu 4:30 
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 operations, control structures, arrays, functions, recursion, dynamic memory allocation, text 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 (sections 0103) concurrently. Students may receive credit for 600.107 or 600.112, but not both. (www.cs.jhu.edu/~joanne/cs107) Prereq: familiarity with computers. 
MW 1:302:45 
600.108 (E) 
INTRO PROGRAMMING LAB (1) More/Froehlich Satisfactory/Unsatisfactory only. Must be taken in conjunction with 600.107 or 600.112. The purpose of this course is to give novice programmers extra handson practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Sections 13 are for Java students (600.107), sections 46 are for Python students (600.112). Coreq: 600.107 or 600.112. 
Sec 1: Wed 6:009:00p, limit 24 
600.112 (E) 
INTRODUCTION TO PROGRAMMING FOR SCIENTISTS AND ENGINEERS (3) Froehlich An introductory "learning by doing" programming course for scientists, engineers, and everybody else who will need basic programming skills in their studies and careers. We cover the fundamentals of structured, modular, and (to some extent) objectoriented programming as well as important design principles and software development techniques such as unit testing and revision control. We will apply our shiny new programming skills by developing computational solutions to a number of realworld problems from a variety of disciplines. Students new to computer programming are encouraged to enroll into 600.108 Intro Programming Lab concurrently with this course. Students may receive credit for 600.107 or 600.111 or 600.112, but not more than one. [Note: This course may not be used for the CS major or minor requirements, except as a substitute for 600.107.] Prereq: none. 
MW 121:15 
600.120 (E)

INTERMEDIATE PROGRAMMING (4) Selinski/Smith/Teichert 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 1:302:45 
600.226 (E,Q) 
DATA STRUCTURES (4) More 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 or 600.107 or 600.120 or equivalent. 
MWF 121:15 
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: intro programming. Students may receive credit for only one of 600.233, 600.333 or 600.433. 
MWF 10 
600.255 (E) 
INTRODUCTION TO VIDEO GAME DESIGN (3) Froehlich A broad survey course in video game design (as opposed to mathematical game theory), covering artistic, technical, as well as sociological aspects of video games. Students will learn about the history of video games, archetypal game styles, computer graphics and programming, user interface and interaction design, graphical design, spatial and object design, character animation, basic game physics, plot and character development, as well as psychological and sociological impact of games. Students will design and implement an experimental video game in interdisciplinary teams of 34 students as part of a semesterlong project. Prereq: sophomores and above, permission of instructor; Coreq: 600.256. Section 1 requires technical skills, including at least one programming course (preferably 2 or more). Section 2 requires artistic skills, including at least one multimedia course (preferably 2 or more). 
MW 4:305:45 
600.256 (E) 
INTRODUCTION TO VIDEO GAME DESIGN LAB (1) Froehlich A lab course in support of 600.255: Introduction to Video Game Design covering a variety of multimedia techniques and applications from image processing, through sound design, to 3D modeling and animation. See 600.255: Introduction to Video Game Design for details about enrolling. Unlike in 600.255, the sections for the lab are meant to have a crosssection of students from different backgrounds. Ideally students working on a team project will be enrolled in the same lab section. Coreq: 600.255. 
Sec 1: M 69 
600.315 (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: 600.226. Students may receive credit for 600.315 or 600.415, but not both. 
TuTh 34:15 
600.321 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Palmer 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] (http://pl.cs.jhu.edu/oose/index.shtml) Prereq: 600.226 and 600.120. Students may receive credit for 600.321 or 600.421, but not both. 
MW 1:302:45 
600.337 (E) 
The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive handson experience as well as considerable theoretical background. Topics include basic communication protocols, synchronous and asynchronous models for consensus, multicast and group communication protocols, distributed transactions, replication and resilient replication, overlay and wireless mesh networks, peer to peer and probabilistic protocols. This course is taught every other Fall semester and is a good introduction course to the 600.667 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. [Systems] (www.cnds.jhu.edu/courses/cs437) Prereq: 600.120, 600.226. Students may receive credit for 600.337 or 600.437, but not both. 
TuTh 34:15 
600.357 (E,Q)

COMPUTER GRAPHICS (3) Kazhdan This course introduces computer graphics techniques and applications, including image processing, rendering, modeling and animation. Students may receive credit for 600.357 or 600.457, but not both. [Applications] Prereq: 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a prerequisite. 
MWF 11 
600.363 (E,Q) 
INTRODUCTION TO 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: 600.226 and 550.171 or Perm. Req'd. Students may receive credit for 600.363 or 600.463, but not both. 
TuTh 1:302:45 
600.415 (E) 
DATABASES (3) Yarowsky Graduate level version of 600.315. Students may receive credit for 600.315 or 600.415, but not both. [Systems] (www.cs.jhu.edu/~yarowsky/cs415.html) Prereq: 600.226. 
TuTh 34:15 
600.421 (E) 
OBJECT ORIENTED SOFTWARE ENGINEERING (3) Palmer Graduate level version of 600.321. Students may receive credit for 600.321 or 600.421, but not both. [Systems or Applications] (http://pl.cs.jhu.edu/oose/index.shtml) Prereq: 600.226 and 600.120. 
MW 1:302:45 
600.437 (E) 
Graduate version of 600.337. This course is taught every other Fall semester and is a good introduction course to the 600.667 Advanced Distributed Systems and Networks projectfocused course that is offered in the following Spring with an eye toward entrepreneurship. Prereq: intermediate programming in C/C++ and data structures. Students may receive credit for 600.337 or 600.437, but not both. [Systems] (www.cnds.jhu.edu/courses/cs437) Prereq: 600.120, 600.226. 
TuTh 34:15 
600.439 (E) 
COMPUTATIONAL GENOMICS (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 projects. [Applications] Prereq: 600.120 & 600.226. 
TuTh 121:15 
600.442 (E,Q) 
MODERN CRYPTOGRAPHY (3) Pappacena This course focuses on cryptographic algorithms, formal definitions, hardness assumptions, and proofs of security. Topics include numbertheoretic problems, pseudorandomness, block and stream ciphers, publickey cryptography, message authentication codes, and digital signatures. [Analysis] Prerequisite: 600.226 and a 300level or above systems course; 600.271/471 and 550.171 or equiv. 
TuTh 4:305:45 
600.443 (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: A basic course in operating systems and networking, or permission of instructor. 
MW 1:302:45 
600.445 (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] (Graduate students should enroll with course number 600.645.) Students may earn credit for 600.445 or 600.645, but not both. (http://www.cisst.org/~cista/445/index.html) Prereq: 600.226 and linear algebra, or permission. Recmd: 600.120, 600.457, 600.461, image processing. 
TuTh 1:302:45 
600.457 (E,Q)

COMPUTER GRAPHICS (3) Kazhdan Graduate level version of 600.357. Students may receive credit for 600.357 or 600.457, but not both. [Applications] Prereq: no audits; 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a prerequisite. 
MWF 11 
600.460 (E)

SOFTWARE VULNERABILITY ANALYSIS (3) Checkoway [Crosslisted in JHUISI.] This course will examine vulnerabilities in C source, stack overflows, writing shell code, etc. Also, vulnerabilities in web applications: SQL Injection, cookies, as well as vulnerabilities in C binary fuzzing, and exploit development without source among other topics. [Applications] 
TuTh 1:302:45 
600.461 (E,Q) 
COMPUTER VISION (3) Vidal [Formerly 600.361] This course gives 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. Edge detection and color perception are covered as well. Elements of machine vision and biological vision are also included. Students can earn credit for at most one of 600.361/461/661. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat. 
MW 121:15 
600.463 (E,Q) 
ALGORITHMS I (3) Dinitz Graduate version of 600.363. Students may receive credit for 600.363 or 600.463, but not both. [Analysis] Prereq: 600.226 and 550.171 or Perm. req'd. 
TuTh 1:302:45 
600.464 (E,Q) 
RANDOMIZED 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] (www.cs.jhu.edu/~cs464) Prereq: 600.363 or 600.463. Students may receive credit for 600.464 or 600.664, but not both. 
TuTh 910:15 
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 
600.471 (EQ) 
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 600.271 and 600.471, unless one is for an undergrad degree and the other for grad. [Analysis] Prereq: discrete math or permission. 
TuTh 121:15 
600.475 (E) 
INTRODUCTION TO 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. 
TuTh 1:302:45 
600.476 (EQ) 
MACHINE LEARNING: DATA TO MODELS (3) Saria
Alt title: How to Become a Data Ninja Prereqs: When in doubt, send the instructor a copy of your transcript to see if the class is appropriate for you. Also, sit through the first few sessions and first homework to get a sense of fit. 1) Students will be asked to do assignments in Matlab. Matlab is typically easy to pick up if one is already familiar with a different programming language. 2) Students are expected to be mathematically mature. One should have taken at least an introductory course in probability and linear algebra. Though not required, exposure to optimization or machine learning is recommended. 3) Proficiency in at least one programming language is expected. 
cancelled for fall, will be offered
spring15 
600.479 (E) NEW COURSE! 
REPRESENTATION LEARNING (3) Arora
Often the success of a machine learning project depends on the choice
of features used. Machine learning has made great progress in
training classification, regression and recognition systems when
"good" representations, or features, of input data are
available. However, much human effort is spent on designing good
features which are usually knowledgebased and engineered by domain
experts over years of trial and error. A natural question to ask then
is "Can we automate the learning of useful features from raw
data?" Representation learning algorithms such as principal component
analysis aim at discovering better representations of inputs by
learning transformations of data that disentangle factors of variation
in data while retaining most of the information. The success of such
datadriven approaches to feature learning depends not only on how
much data we can process but also on how well the features that we
learn correlate with the underlying unknown labels (semantic content
in the data). This course will focus on scalable machine learning
approaches for learning representations from large amounts of
unlabeled, multimodal, and heterogeneous data. We will cover topics
including deep learning, multiview learning, dimensionality
reduction, similaritybased learning, and spectral learning.
[Analysis or Applications] Students may receive credit for 600.479 or
600.679 but not both.
Required course background: machine learning or basic probability and linear algebra; mathematical maturity. 
TuTh 3:004:15 
600.491 (E) 
COMPUTER SCIENCE WORKSHOP I 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 
600.503 
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 
600.507 
INDEPENDENT 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 
600.509 
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 
600.519 
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 
600.546 (E) 
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 
600.601 
Required for all fulltime PhD students. Recommended for MSE students. 
TuTh 10:3012 
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 
600.615 
BIG DATA, SMALL LANGUAGES, SCALABLE SYSTEMS Ahmad This class will study domainspecific data management tools, focusing on extremely scalable system design based on the domain's semantic and structural properties. We will study a variety of data models including stream, graph, array and probabilistic data, and their processing on modern architectures such as column and keyvalue stores, stream and XQuery engines. Further topics include the use of novel hardware such as solid state disks, phase change memory, GPUs, and FPGAs. The class includes a semester long group project to develop a query processor for an application of the group's choice (e.g. on system log, finance, web, sensor, speech data). [Systems] (www.cs.jhu.edu/~yanif/teaching/bdslss) Prereq: 600.315/415 or equivalent. 
MW 121:15 
600.624 (E) NEW COURSE! 
ADVANCED TOPICS IN DATAINTENSIVE COMPUTING (3) Burns The advent of cloud computing has lead to an explosion of storage system and data analysis software, including NoSQL databases, bulksynchronous processing, graph computing engines, and stream processing. This course will explore scaleout software architectures for dataprocessing tasks. It will examine the algorithms and datastructures that underlie scalable systems and look at how hardware and networking trends influence the design and deployment of cloud computing. Recommended Course Background: EN.600.320/420 or permission of instructor. [Systems] Prereq: 600.320/420. 
TuTh 4:305:45 
600.639 
COMPUTATIONAL GENOMICS Langmead Graduate version of 600.439. Students may earn credit for 600.439 or 600.639, but not both. [Applications] Prereq: 600.120 & 600.226. 
TuTh 121:15 
600.645 (E) 
COMPUTER INTEGRATED SURGERY I Taylor Graduate version of 600.445 (see description). Students may earn credit for 600.445 or 600.645, but not both. [Applications] (http://www.cisst.org/~cista/445/index.html) Prereq: data structures and linear algebra, or permission. Recommended: intermediate programming in C/C++, 600.457, 600.461, image processing. 
TuTh 1:302:45 
600.661 (E,Q) 
COMPUTER VISION (3) Vidal [Formerly 600.461.] Graduate version of 600.461. Students may receive credit for at most one of 600.361/461/661. [Applications] (https://cirl.lcsr.jhu.edu/Vision_Syllabus) Prereq: intro programming, linear algebra, prob/stat 
MW 121:15 
600.664 
RANDOMIZED ALGORITHMS (3) Braverman Graduate level version of 600.464. [Analysis] (www.cs.jhu.edu/~cs464) Prereq: 600.363 or 600.463. Students may receive credit for 600.464 or 600.664, but not both. 
TuTh 9:0010:15 
600.676 
MACHINE LEARNING: DATA TO MODELS Saria
Alt title: How to Become a Data Ninja Prereqs: 1) Proficiency in at least one programming language is expected. 2) At least introductory courses in probability and linear algebra. 
cancelled for fall, will be offered
spring15 
600.679 NEW COURSE! 
REPRESENTATION LEARNING (3) Arora
Graduate student version of 600.479. Students may receive credit for
600.479 or 600.679 but not both. [Analysis or Applications] Required course background: machine learning or basic probability and linear algebra; mathematical maturity. 
TuTh 3:004:15 
600.684 NEW COURSE! 
MEDICAL 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] Required course background: intermediate programming (C/C++), data structures, linear algebra. 
Fr 911:30 
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 
600.726 
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. Prereq: permission of instructor. 
tbd 
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. 
tbd 
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 
600.757  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. 
tbd 
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. 
Wed 4 
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. 
Th 12 
600.766 
SELECTED TOPICS IN MEANING, TRANSLATION AND GENERATION OF TEXT VanDurme This weekly reading group will review current research and survey articles on the topics of computational semantics, statistical machine translation, and natural language generation. Enrolled students will present papers and lead discussions. 
Fr 10:3011:30 
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. 
Cancelled 
600.772 
SELECTED TOPICS IN LINEAR PROGRAMMING AND SEMIDEFINITE PROGRAMMING Li Linear programming and semidefinite programming are powerful techniques in convex optimization. They have been used to achieve the best known approximation results for many important combinatorial optimization problems, such as vertex cover, max cut, sparsest cut and MAX2SAT. In this course we will together explore the applications of these techniques in computer science, as well as some recent results about their limitations. Time permitting, we may also discuss their connections to the wellknown unique games conjecture. This course will be in the form of a reading group, and students are encouraged to select a paper and lead a discussion. Prereq: 600.463, 600.464 or permission. 
Cancelled 
600.801 
DISSERTATION RESEARCH 
See below for faculty section
numbers. 
600.803 
GRADUATE RESEARCH Independent research for masters or predissertation PhD students. Permission required. 
See below for faculty section
numbers. 
600.809 
INDEPENDENT STUDY (graduate students) 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