Fall 2016 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 (2) Freeman

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.

Th 4:30-6:15
limit 40, CS majors only

600.107 (E)

INTRO TO PROGRAMMING IN JAVA (3) Selinski

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 concurrently in Fall/Spring semesters.

Prereq: familiarity with computers. Students may receive credit for 600.107 or 600.112, but not both.

Sec 01: MW 1:30-2:45, limit 120
Sec 02: MW 3-4:15, limit 80
Sec 03: MW 3-4:15, limit 40, CS freshmen only

600.108 (E)

INTRO PROGRAMMING LAB (1) Selinski

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 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. Students may receive credit for 600.108 or 600.113, but not both.

Co-req: 600.107.

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

600.120 (E)

INTERMEDIATE PROGRAMMING (4) More/Mitchell

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 (More): MWF 12:00-1:15 CS/CE majors/minors only
Sec 02 (More): MWF 1:30-2:45 CS freshmen only
Sec 03 (Mitchell): MWF 3:00-4:15
limit 33/section

600.226 (E,Q)

DATA STRUCTURES (4) Froehlich/Schatz

This course covers the design, implementation and efficiencies of data structures and associated algorithms, including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees and graphs. Other topics include sorting, hashing, Java generics, and unit testing. Course work involves both written homework and Java programming assignments.

Prereq: AP CS or 600.107 or 600.120 or equivalent.

Sec 01 (Froehlich): MWF 12-1:15, limit 75
Sec 02 (Schatz): MWF 1:30-2:45, CS/CE majors/minors only, limit 50

600.233 (E)

COMPUTER SYSTEM FUNDAMENTALS (3) Koehn

[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: 600.120. 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 30

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

600.271 (E,Q)

AUTOMATA and COMPUTATION THEORY (3) More

This course is an introduction to the theory of computing. topics include design of finite state automata, pushdown automata, linear bounded automata, Turing machines and phrase structure grammars; correspondence between automata and grammars; computable functions, decidable and undecidable problems, P and NP problems, NP-completeness, and randomization. Students may not receive credit for 600.271 and 600.471 for the same degree.

Prereq: 550.171.

TuTh 1:30-2:45
limit 75

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

600.318 (E)

OPERATING SYSTEMS (3) Froehlich

This course covers the fundamental topics related to operating systems theory and practice. Topics include processor management, storage management, concurrency control, multi-programming 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 600.233. 600.211 Recommended. Students may receive credit for 600.318 or 600.418, but not both.

MWF 10
limit 20

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 cutting-edge 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 multi-core processors. The course will examine different forms of parallelism in four sections. These are: (1) massive data-parallel computations with Hadoop!; (2) programming compute clusters with MPI; (3) thread-level 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; 600.233 recommended. Students may receive credit for 600.320 or 600.420, but not both.

TuTh 4:30-5:45
limit 30

600.321 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith

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

600.365 (E)

NEW COURSE!

KNOWLEDGE DISCOVERY FROM TEXT (3) VanDurme & Lippincott

The world is full of text: webpages, emails, newspaper articles, tweets, medical records, and so on. The purpose of text is for people to convey knowledge to other people. This course focuses on how computers analyze large, potentially streaming, text collections to automatically discover knowledge on their own (and to help people better find it themselves). Lectures and assignments will cover relevant topics in automatic classification (applied machine learning), linguistics, high-performance computing, and systems engineering, working with software systems for automatic question answering, populating knowledge bases, and aggregate analysis of social media such as Twitter. [Applications]

Prereqs: 600.120 & 600.226.

TuTh 3-4:15
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

600.418 (E)

OPERATING SYSTEMS (3) Froehlich

Similar material as 600.318, covered in more depth. Intended for upper-level undergraduates and graduate students. [Systems]

Required course background: 600.226 and 600.233; 600.211 recommended. Students may receive credit for 600.318 or 600.418, but not both.

MWF 10
limit 25

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.

TuTh 4:30-5:45
limit 40

600.421 (E)

OBJECT ORIENTED SOFTWARE ENGINEERING (3) Smith

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

600.424 (E)
ADDED!

NETWORK SECURITY (3) Nielson

[Cross-listed 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.

MW 3-4:15
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 25

600.442 (E,Q)

MODERN CRYPTOGRAPHY (3) Jain

Modern Cryptography includes seemingly paradoxical notions such as communicating privately without a shared secret, proving things without leaking knowledge, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as zero-knowledge proofs, functional encryption and program obfuscation. The class will focus on rigorous proofs and require mathematical maturity. [Analysis]

Prerequisite: 600.363/463, 600.271/471 and 550.171 or equiv.

MW 1:30-2:45
limit 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 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.

TuTh 9-10:15
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 45

600.454 (E)

PRACTICAL CRYPTOGRAPHIC SYSTEMS (3) Green

[Co-listed with 650.445.] This semester-long course will teach systems and cryptographic design principles by example: by studying and identifying flaws in widely-deployed 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 reverse-engineering undocumented cryptographic systems. [Systems]

MW 12-1:15
limit 25

600.457 (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: no audits; 600.120 (C++), 600.226, linear algebra. Permission of instructor is required for students not satisfying a pre-requisite. Students may receive credit for 600.357 or 600.457, but not both.

MWF 11
limit 40

600.461 (E,Q)

COMPUTER VISION (3) Reiter

[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.

TuTh 12-1:15
limit 20

600.463 (E,Q)

INTRO ALGORITHMS (3) Dinitz

This course concentrates on the design of algorithms and the rigorous analysis of their efficiency. topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications (such as 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
Sec 01: undergrads, limit 45
Sec 02: grad students, limit 30

600.464 (E,Q)

RANDOMIZED & BIG DATA ALGORITHMS (3) Braverman

The course emphasizes algorithmic design aspects, and how randomization can be a helpful tool. The topics covered includee: tail inequalities, linear programming relaxation & randomized rounding, 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: 600.363/463 and Probability. Students may receive credit for 600.464 or 600.664, but not both.

TuTh 12-1:15
limit 40

600.465 (E)

NATURAL LANGUAGE PROCESSING (4) 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, section T 6-7:30p
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 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 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]

Required course background: multivariate calculus.

MW 1:30
limit 75

600.477 (E,Q)

NEW COURSE!

CAUSAL INFERENCE (3) Shpitser

"Big data" is not necessarily "high quality data." Systematically missing records, unobserved confounders, and selection effects present in many datasets make it harder than ever to answer scientifically meaningful questions. This course will teach mathematical tools to help you reason about causes, effects, and bias sources in data with confidence. We will use graphical causal models, and potential outcomes to formalize what causal effects mean, describe how to express these effects as functions of observed data, and use regression model techniques to estimate them. We will consider techniques for handling missing values, structure learning algorithms for inferring causal directionality from data, and connections between causal inference and reinforcement learning. [Analysis]

Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for 600.477 or 600.677, but not both.

TuTh 1:30-2:45
limit 20

600.479 (E)

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 40

600.485 (Q)

NEW COURSE!

PROBABILISTIC MODELS OF THE VISUAL CORTEX (3) Yuille

[Co-listed with AS.050.375 and AS.050.675.] The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks. [Applications or Analysis]

Pre-requisites: Calc I, programming experience (Python preferred).

TuTh 9-10:15
limit 10

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

UNDERGRADUATE RESEARCH

Independent research for undergraduates under the direction of a faculty member in the department. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

See below for faculty section numbers and whether to select 507 or 517.

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

600.517

GROUP UNDERGRADUATE RESEARCH

Independent research for undergraduates under the direction of a faculty member in the department. This course has a weekly research group meeting that students are expected to attend. The program of research, including the credit to be assigned, must be worked out in advance between the student and the faculty member involved.

Permission required.

Only for faculty specifically marked below.

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

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

600.591

COMPUTER SCIENCE WORKSHOP I

[Formerly 600.491] 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

600.601

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 40

600.629

NEW COURSE!

WIRELESS NETWORKS (3) Zadeh

This course covers the basics of mobile communication and wireless networking for computer science majors by keeping a balance between communication and networking topics. In this course, the students will be exposed to wireless transmission fundamentals (path loss, shadowing, modulation, coding and channel models), and learn about medium access control protocols, wireless local area networks (IEEE 802.11), and wireless mobile networks and applications, including cellular networks (cellular concept, channel reuse, capacity limits, and cellular systems), fourth generation systems, long term evolution (LTE), mobile applications, and mobile IP. [Systems]

Prerequisites: 600.344/444 recommended.

MW 1:30
limit 30

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 25

600.641

ADVANCED TOPICS IN GENOMIC DATA ANALYSIS Battle

[Formerly titled Machine Learning for Genomic Data - Trends and Applications]
Genomic data is becoming available in large quantities, but understanding how genetics contributes to human disease and other traits remains a major challenge. Machine learning approaches allow us to automatically analyze and combine genomic data, build predictive models, and identify genetic elements important to disease and cellular processes. This course will cover uses of machine learning in diverse genomic applications. Students will present and discuss current literature. Topics include predicting disease risk from genomic data, integrating diverse genomic data types, gene network reconstruction, and other topics guided by student interest. The course will include a project component with the opportunity to explore publicly available genomic data. [Applications]

Recommended Course Background: coursework in data mining, machine learning. Students may receive credit for 600.441 or 600.641, but not both.

TuTh 9-10:15
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

COMPUTER VISION (3) Reiter

[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

TuTh 12-1:15
limit 40

600.677

NEW COURSE!

CAUSAL INFERENCE (3) Shpitser

Advanced graduate version of 600.477. [Analysis]

Pre-requisites: familiarity with the R programming language, multivariate calculus, basics of linear algebra and probability. Students may receive credit for 600.477 or 600.677, but not both.

TuTh 1:30
limit 20

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.

Th 1-2
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.

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 15

600.760

CS THEORY SEMINAR Braverman, Dinitz, Li

Seminar series in theoretical computer science. Topics include algorithms, complexity theory, and related areas of TCS. Speakers will be a mix of internal and external researchers, mostly presenting recently published research papers.

W 12
limit 30

600.764

CANCELLED

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.

cancelled
limit 15

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 15

600.766

SELECTED TOPICS IN COMPUTATIONAL SEMANTICS VanDurme & Rawlins

A seminar focussed on current research and survey articles on computational semantics.

Fr 10-10:50
limit 15

600.768

SELECTED TOPICS IN MACHINE TRANSLATION Koehn

Students in this course will review, present, and discuss current research in machine translation.

Prereq: permission of instructor.

T 10-11
limit 15

AS.050.814

NEW COURSE!

RESEARCH SEMINAR IN COMPUTER VISION Yuille

This course covers advanced topics in computational vision. It discusses and reviews recent progress and technical advances in visual topics such as object recognition, scene understanding, and image parsing.

tba

600.801

PHD RESEARCH

Independent research for PhD students.

See below for faculty section numbers.

600.803

MASTERS RESEARCH

Independent research for masters students. Permission required.

See below for faculty section numbers.

600.807

TEACHING PRACTICUM Selinski

PhD students will gain valuable teaching experience, working closely with their assigned faculty supervisor. Successful completion of this course fulfills the PhD teaching requirement. Permission required.

limit 25

600.809

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 (ugrad research use 517, not 507)
05 - Scott Smith
06 - Joanne Selinski
07 - Harold Lehmann
08 - John Sheppard
09 - Greg Hager
10 - Gregory Chirikjian
11 - Sanjeev Khudhanpur
12 - Yair Amir
13 - David Yarowsky
14 - Noah Cowan
15 - Randal Burns
16 - Jason Eisner (ugrad research use 517, not 507)
17 - Mark Dredze
18 - Michael Dinitz
19 - Rachel Karchin
20 - Michael Schatz
21 - Avi Rubin
22 - Matt Green
23 - Andreas Terzis
24 - Raman Arora (ugrad research use 517, not 507)
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 (ugrad research use 517, not 507)
36 - Emad Boctor (ugrad research use 517, not 507)
37 - Joel Bader
38 - Ben VanDurme
39 - Jeff Siewerdsen
40 - Vladimir Braverman
41 - Suchi Saria
42 - Ben Langmead
43 - Steven Salzberg
44 - [ Stephen Checkoway ]
45 - Liliana Florea
46 - Adam Lopez
47 - Philipp Koehn
48 - Abhishek Jain
49 - Anton Dabhura (ugrad research use 517, not 507)
50 - Joshua Vogelstein
51 - Ilya Shpitser
52 - Austin Reiter
53 - Tamas Budavari
54 - Alan Yuille
55 - Peter Freeman