Fall 2014 Courses

  • See the calendar layout for a convenient listing of course times and room requests.
  • Click here for a printable version of this table only.

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 6-8p, alternate weeks
Sec 02: Tue 6-8p, alternate weeks
limit 20 each, CS majors only

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 3-week blocks of meetings with different computer science professors, focused on a central theme. Active participation is required. Satisfactory/Unsatisfactory only.

Tu 4:30
limit 50, CS majors only!

600.107 (E)

INTRO TO PROGRAMMING IN JAVA (3) More

This course introduces fundamental structured and object-oriented 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 object-oriented concepts including inheritance and exceptions as time permits. First-time programmers are strongly advised to take 600.108 (sections 01-03) 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 1:30-2:45
limit 150

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 hands-on practice with guided supervision. Students will work in pairs each week to develop working programs, with checkpoints for each development phase. Sections 1-3 are for Java students (600.107), sections 4-6 are for Python students (600.112).

Co-req: 600.107 or 600.112.

Sec 1: Wed 6:00-9:00p, limit 24
Sec 2: Thu 7:00-10:00p, limit 24
Sec 3: Fri 1:30-4:30p, limit 16
Sec 4: Wed 6:00-9:00p, limit 16
Sec 5: Thu 4:30-7:30p, limit 16
Sec 6: Fri 12:00-3:00p, limit 16

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) object-oriented 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 real-world 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 12-1:15
limit 75

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 low-level programming techniques, as well as object-oriented 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 1:30-2:45
Sec 02: MWF 3:00-4:15, CS majors only
Sec 03: MWF 4:30-5:45
Sec 04: MWF 12:00-1:15
limit 24/section

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 12-1:15
Sec 01: CS majors only
Sec 02: others
limit 35/section

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 8-bit micro-controllers through 32/64-bit 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, super-scalar 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
Sec 01: CS majors only, limit 45
Sec 02: others, limit 20

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 3-4 students as part of a semester-long project.

Prereq: sophomores and above, permission of instructor; Co-req: 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 4:30-5:45
Sec 1: for technical students
Sec 2: for non-technical students
limit 20/section

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 multi-media 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 cross-section of students from different backgrounds. Ideally students working on a team project will be enrolled in the same lab section.

Co-req: 600.255.

Sec 1: M 6-9
Sec 2: T 6-9
Sec 3: W 6-9
Sec 4: H 6-9
limit 12/section

600.315 (E)

DATABASES (3) Yarowsky

Introduction to database management systems and database design, focusing on the relational and object-oriented 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 3-4:15
limit 30

600.321 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Palmer

This course covers object-oriented 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 object-oriented 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 1:30-2:45
limit 40

600.337 (E)

DISTRIBUTED SYSTEMS (3) Amir

The course teaches how to design and implement efficient tools, protocols and systems in a distributed environment. The course provides extensive hands-on 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 project-focused 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 3-4:15
limit 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 pre-requisite.

MWF 11
limit 20

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 union-find); graph algorithms and searching techniques such as minimum spanning trees, depth-first 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 1:30-2:45
limit 30

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 3-4:15
limit 40

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 1:30-2:45
limit 40

600.437 (E)

DISTRIBUTED SYSTEMS (3) Amir

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 project-focused 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 3-4:15
limit 30

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, real-world 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 12-1:15
limit 20

600.442 (E,Q)

MODERN CRYPTOGRAPHY (3) Pappacena

This course focuses on cryptographic algorithms, formal definitions, hardness assumptions, and proofs of security. Topics include number-theoretic problems, pseudo-randomness, block and stream ciphers, public-key cryptography, message authentication codes, and digital signatures. [Analysis]

Prerequisite: 600.226 and a 300-level or above systems course; 600.271/471 and 550.171 or equiv.

TuTh 4:30-5:45
limit 30

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 semester-long 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 1:30-2:45
limit 45

600.445 (E)

COMPUTER INTEGRATED SURGERY I (4) Taylor

This course focuses on computer-based 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 follow-up. It emphasizes the relationship between problem definition, computer-based 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 1:30-2:45
limit 30

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 pre-requisite.

MWF 11
limit 20

600.460 (E)

SOFTWARE VULNERABILITY ANALYSIS (3) Checkoway

[Cross-listed 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 1:30-2:45
limit 40

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 3-D 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 12-1:15
limit 20

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 1:30-2:45
limit 30

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, de-randomization, existence proofs, universal hashing, markov chains, metropolis and metropolis-hastings methods, mixing by coupling and by eigenvalues, counting problems, semi-definite 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 9-10:15
limit 20

600.465 (E)

NATURAL LANGUAGE PROCESSING (3) Eisner

This course is an in-depth 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 (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech 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 3-4:15
limit 40

600.471 (EQ)

THEORY OF COMPUTATION (3) Li

This is a graduate-level course studying the theoretical foundations of computer science. Topics covered will be models of com-putation 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 12-1:15
limit 40

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 semi-supervised learning. Additional topics may include reinforcement learning and learning theory. The course will also consider challenges resulting from learning applications, such as transfer learning, multi-task learning and large datasets. We will cover popular algorithms (naive Bayes, SVM, perceptron, HMM, winnow, LDA, k-means, 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]
(syllabus.html)

Required course background: multivariate calculus.

TuTh 1:30-2:45
limit 75

600.476 (EQ)

MACHINE LEARNING: DATA TO MODELS (3) Saria

Alt title: How to Become a Data Ninja
How can robots locate themselves in an indoor environment when navigating? How do you infer which patients need attention first in the ICU? How can one identify the start of an epidemic using tweets? How does one predict the way a traffic jam will spread through the local streets during an Orioles game? How can you communicate with your TV using only hand gestures? This class will cover the fundamental concepts of Probabilistic Graphical Models as a representation framework for addressing questions like the ones above. We will study algorithms for model estimation, exact and approximate inference. The class will have 4 interactive sessions during which students will learn through an open discussion format how to think about example open-ended real-world problems with the tools learnt in class. Students are also required to tackle a project of their choice within which they will experiment with the ideas learnt in class. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both.

Pre-reqs: 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
MW 1:30-2:45
limit 15

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 knowledge-based 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 data-driven 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, multi-modal, and heterogeneous data. We will cover topics including deep learning, multi-view learning, dimensionality reduction, similarity-based 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 3:00-4:15
limit 20

600.491

(E)

COMPUTER SCIENCE WORKSHOP I

An applications-oriented, 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 computer-integrated 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

COMPUTER SCIENCE SEMINAR

Required for all full-time PhD students. Recommended for MSE students.

TuTh 10:30-12
limit 90

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 12-1:15
limit 30

600.615

BIG DATA, SMALL LANGUAGES, SCALABLE SYSTEMS Ahmad

This class will study domain-specific 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 key-value 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)

Pre-req: 600.315/415 or equivalent.

MW 12-1:15
limit 30

600.624 (E)

NEW COURSE!

ADVANCED TOPICS IN DATA-INTENSIVE COMPUTING (3) Burns

The advent of cloud computing has lead to an explosion of storage system and data analysis software, including NoSQL databases, bulk-synchronous processing, graph computing engines, and stream processing. This course will explore scale-out software architectures for data-processing tasks. It will examine the algorithms and data-structures 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 4:30-5:45
limit 16

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 12-1:15
limit 20

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 1:30-2:45
limit 30

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 12-1:15
limit 50

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 9:00-10:15
limit 20

600.676

MACHINE LEARNING: DATA TO MODELS Saria

Alt title: How to Become a Data Ninja
Graduate version of 600.476. [Analysis or Applications] Students may receive credit for 600.476 or 600.676, but not both.

Pre-reqs: 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
MW 1:30-2:45
limit 20

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 3:00-4:15
limit 20

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, multi-modal 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 9-11:30
limit 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
limit 15

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
limit 15

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
limit 15

500.745

SEMINAR IN COMPUTATIONAL SENSING AND ROBOTICS Kazanzides, Whitcomb, Vidal, Etienne-Cummings

Seminar series in robotics. Topics include: Medical robotics, including computer-integrated surgical systems and image-guided 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. Human-machine systems, including haptic and visual feedback, human perception, cognition and decision making, and human-machine collaborative systems. Cross-listed with Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Biomedical Engineering.

Wed 12-1:30
limit 80

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
limit 20

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
limit 20

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
limit 20

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 10:30-11:30
limit 20

600.771

PROBABILITY ON STRINGS, TREES, AND SEQUENCES Lopez

Many areas of practical computer science focus on discrete data that is sequential or tree-shaped: 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, decision-making 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
limit 20

600.772

SELECTED TOPICS IN LINEAR PROGRAMMING AND SEMI-DEFINITE PROGRAMMING Li

Linear programming and semi-definite 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 MAX-2-SAT. 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 well-known 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
limit 10

600.801

DISSERTATION RESEARCH

See below for faculty section numbers.

600.803

GRADUATE RESEARCH

Independent research for masters or pre-dissertation PhD students. Permission required.

See below for faculty section numbers.

600.809

INDEPENDENT STUDY (graduate students)

Permission required.

See below for faculty section numbers.

Faculty section numbers for all independent type courses, undergraduate and graduate.

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 Callison-Burch
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