Graduate Course Information

The following pages contain descriptions of the courses that are available in the Graduate Program in Computer Science. For each course, it is indicated in which term (Fall and/orSpring) the course is usually given, and the number of credits provided. Note that more advanced graduate courses are usually not given every year.

The first courses listed are 300 and 400 level courses and are, strictly speaking, undergraduate courses. However, the Graduate Program in Computer Science has approved of them (and only them) for credit toward a graduate degree; at most two undergraduate courses can be taken for credit towards a graduate degree in computer science. Some of these courses are recommended to students whose background in computer science is weak.

Any 300 and 400 level courses for which the student wishes to have graduate credit must be registered with a G-prefix; write `G' under `credit hour prefix' on the registration card. A student registering for an undergraduate course other than from the approved list must register with an E-prefix: write `E' under `credit hour prefix' on the registration card; this means that the student does not receive graduate credit.

Other special credit registrations: A student who is a Teaching Assistant registers for 6 credits of 198:877 with an E-prefix. A student who is a Graduate Assistant registers for 6 credits of 198:866 with an E-prefix. A student registering for Matriculation Continued registers for 198:800 and no special prefix. The N-prefix means that the course is taken `not for credit' and no exam or grades are given.

Important! Whether or not it is expressly stated in the prerequisites of each 500 level course, it is assumed that the student has had the background required for admission. These will be referred to as `entrance requirements'. Prerequisites are to be taken as strong guidelines that should help a student to decide whether to register in a specific course at a given time in the course of his studies. Normally, students taking a course should satisfy the course prerequisites. However, if a student is unclear about the prerequisites of a course or if he has doubts about his qualifications for the course, he should consult the instructor in charge. Although special cases will be considered individually by the instructor, students who have not had the prerequisites should not expect to be allowed to take the course.

 

Another important point is about the maximum number of credits for which you are allowed to register. See the sections on Course Load for those constraints. The total number of credits for which you register includes all courses. Thus, your TAship or GAship credits, your PALS credits and your regular courses are all counted.

The description of all courses available for graduate credit can be found at the Course Synopses section.

Before taking a course outside the Department of Computer Science, the student needs to consult with his advisor and receive his approval.

Normally an M.Sc. student may take no more than 6 credits outside the department. (Please note that the courses listed here may have prerequisites that are not listed here.)

Analytics and Information Management (MBA Curriculum)

960:641

Analytics for Business Intelligence

Applied Mathematics

642:516	Applied Partial Differential Equations
642:527,8	Methods of Applied Mathematics
642:550	Linear Algebra and Applications
642:551	Applied Algebra
642:575	Numerical Solutions of Partial Differential Equations
642:577,8	Selected Topics in Systems Theory
642:581	Applied Graph Theory
642:582,3	Combinatorics I, II
642:587	Selected Topics in Discrete Mathematics
642:588	Introduction to Mathematical Techniques in Operations Research
642:589	Topics in Mathematical Techniques in Operations Research
642:591,2	Topics in Probability and Ergodic Theory
642:611	Special Topics in Applied Mathematics

Biomedical Engineering

526:520	Neuroelectric Sytems
125:526	Brain Dynamics
125:610	Advanced Topcs in Computers in Biomedical Engineering
125:620	Neural Networks and Neurocomputing

Cognitive Science

185:500

Cognitive Science Proseminar

Electrical and Computer Engineering

332:505,6	Control Systems Theory I,II
332:526	Robotics Systems Engineering
332:527	Digital Speech Processing
332:529	Image Coding and Processing
332:539	Advanced Topics in Digital Signal Processing: Introduction to Functional Neuroimaging, Methods and Data Analysis
332:542	Information Theory and Coding
332:543,4	Communication Networks I,II
332:553	Wireless Access to Information Networks
332:560	Computer Graphics
332:561	Machine Vision
332:562	Visualization and Advanced Computer Graphics
332:563,4	Computer Architecture I,II
332:565	Neurocomputer System Design
332:566	Parallel and Distributed Computing
332:568	Software Engineering Web Applications (3)
332:570	Robust Computer Vision
332:571	Virtual Reality Technology
332:574	Computer Aided Digital VLSI Design
332:575	VLSI Array Processors
332:576	Testing of Ultra Large Scale Circuits
332:588	Integrated Transistor-Circuit Design

Industrial Engineering

540:515	Stochastic Models
540:525	Applied Queuing Theory
540:540	Computational Methods
540:575	Advanced Engineering Economics
540:585,685	Systems Reliability Engineering I, II

Linguistics

615:510/1/4	Syntax I,II and Topics
615:520/1/4	Phonology I,II and Topics
615:530/1/4	Semantics I,II and Topics.

Mathematics

640:551	Abstract Algebra
640:561	Mathematical Logic
640:566	Axiomatic Set Theory
640:567	Model Theory
640:569	Selected Topics in Logic

Operations Research

711:513	Discrete Optimization
711:553	Boolean and Pseudo-Boolean Functions
711:556	Queueing Theory
711:558	Convex Analysis and Optimization

Philosophy

730:513	Logic and Natural Language
730:550	Seminar in Epistomology
730:570	Seminar in the Phil. of Language
730:575	Seminar in the Phil. of Mind
730:650	Advanced Topics in Epistomology
730:670	Advanced Topics in the Phil. of Language
730:675	Advanced Topics in the Phil. of Mind
730:676	Advanced Topics in the Phil. of Psychology
730:678	Advanced Topics in Decision Theory
730:679	Topics in Logic

Psychology

830:514	Sensation and Perception
830:515	Computational Vision
830:535	Language and Communication
830:540	Mathematical Models of Learning, Perception, and Cognition
830:546	Memory and Attention
830:547	Computational Models of Cognition
830:550	Language Development
830:602	Psycholinguistics
830:611	Seminar in Perception
830:635	Selected Topics in Learning
830:637	Seminar: Cognition
830:641	Seminar: Thinking

Statistics

960:553	Categorical Data Analysis
960:554	Applied Stochastic Processes
960:555	Nonparametric Statistics
960:563	Regression Analysis
960:565	Applied Time Series Analysis
960:576	Survey Sampling
960:580	Basic Probability
960:582	Introduction to the Methods and Theory of Probability
960:583	Methods of Statistical Inference
960:586	Interpretation of Data I
960:587	Interpretation of Data II
960:588	Data Mining
960:590	Design of Experiments
960:591	Advanced Design of Experiments
960:592	Theory of Probability
960:593	Theory of Statistics
960:595	Intermediate Probability
960:652/3	Advanced Theory of Statistics
960:654	Stochastic Processes
960:681/2	Advanced Probability Theory
960:690	Bayesian Analysis
960:668	Bayesian Data Analysis

Analytics and Information Management (MBA Curriculum)

960:641

Analytics for Business Intelligence

A more complete and up-to-date version is available from the Undergraduate>Course Synopses section of this site.

01:198:110

Principles of Computer Science (3)
Not for major credit. Students planning further study in CS should take 01:198:111.
A general survey about what computers are and how they are used, including an introduction to computer programming. 

01:198:111

Introduction to Computer Science (4)
An intensive introduction to computer science. Writing, debugging, and analyzing programs. Algorithms for sorting and searching. Introduction to data structures. For students in science, mathematics, and engineering.

01:198:112

Data Structures (4)
Prerequisite: 01:198:111.
Queues, stacks, trees, lists, graphs, and recursion; sorting and searching; comparison of algorithms.

01:198:205

Introduction to Discrete Structures I (4)
Prerequisites: 01:198:111 and CALC.
Sets, propositional and predicate logic, relations and their properties, and definitions and proofs by induction with applications to the analysis of loops of programs.

01:198:206

Introduction to Discrete Structures II (4)
Prerequisites: 01:198:205.
Credit will not be given for both 01:198:206 and 01:640:477.

Counting (binomial coefficients, combinations), methods of finding and solving recurrence relations, discrete probability, regular expressions and finite automata, basic graph theory.

01:198:211

Computer Architecture and Assembly Language (4)
Prerequisite: 01:198:112. Credit not given for both this course and 14:330:337.
The levels of organization in digital computer systems; assembly language programming techniques; comparative machine architectures; assemblers, loaders, and operating systems. Programming assignments in assembly language.

01:198:221

Numerical Problems and Computer Programming (4)
Prerequisites: CALC2 or the equivalent.
An introductory numerical analysis course similar to 01:198:323, but designed for nonmajors. FORTRAN taught in recitation.

01:198:230

Introduction to Information Systems (4)
Prerequisite: 01:198:110 or 111.
Not for Major credit. Introduction to databases and file structures, and to network access tools for information storage and retrieva.

01:198:314

Principles of Programming Languages (4)
Prerequisites: 01:198:112, 205.
Syntax, semantics, names and values, control structures, data types, procedures and parameters, scope rules; procedural, functional, pnkect-oriented and logic-programming paradigms.

01:198:323

Numerical Analysis and Computing (4)
Prerequisites: 01:198:111, CALC2, and 01:640:250.
Credit will not be given for both 01:198:323 and 01:640:373.
Approximation, interpolation, numerical differentiation, integration; numerical solution of nonlinear equations, linear algebraic systems, and ordinary differential equations.

01:198:324

Numerical Methods (4)
Prerequisites: 01:198:323 or 01:640:373.
Credit will not be given for both 01:198:324 and 01:640:374.
Computational methods for linear algebraic systems, eigenvalues and eigenvectors, approximation of functions, splines; numerical solution of initial and boundary value problems for differential equations.

01:198:336

Principles of Database Management Systems (4)
Prerequisites: 01:198:112 and 01:640:205
Provides an introduction to the concepts and design principles used in database management systems.

01:198:344

Design and Analysis of Computer Algorithms (4)
Prerequisites: 01:198:112 and 01:198:206.
Study of algorithms. Techniques for efficiency improvement. Analysis of complexity and validity for sorting (internal, external), shortest path, spanning tree, cut points, connected components, and pattern matching. NP completeness.

01:198:352

Internet Technology (4)
Prerequisites: 01:198:211
Provides a practical understanding of computer networks with respect to system architectures, protocols, and client-server interaction. These objectives are accomplished through extensive laboratory exercises.

01:198:405

Seminar in Computers and Society (3)
Prerequisites: At least one computer science course and one course in sociology, political science, anthropology, or philosophy.
Study and discussion of the impact of computers on man and society. For all students interested in exploring the social consequence of computer developments.