Masters Degrees:

Students pursuing the M.S. degree may choose either Plan I (Thesis Plan) or Plan II (Comprehensive Examination Plan).

Students following Plan I must complete the three core courses, two courses chosen from the breadth course list with at most one chosen from the Management and Applications of Technology List, three courses chosen from the concentration course lists with at least one course chosen from at least two different concentrations, two additional courses chosen with the approval of the advisor, and a thesis.

Students following Plan II must complete the three core courses, three courses chosen from the breadth course list with at most two chosen from the Management and Applications of Technology List, four courses chosen from the concentration course lists with at least one course chosen from at least three different concentrations, and two additional courses chosen with the approval of the advisor. Students pursuing this option must pass a comprehensive examination which will be administered through NetSys 295 and will consist of a term paper on a topic relevant to the student's educational program and that term's speakers.

 

Ph.D. Degree:

The Ph.D. degree requires the following thirteen courses: three core courses; three courses chosen from the breadth course list, with at most two chosen from the Management and Applications of Technology list; four courses chosen from the concentration course lists, with at least one course chosen from at least three different concentrations; and three additional courses, chosen with the approval of the research advisor. Students must also complete two teaching practicum courses (ICS 399) and a dissertation.

Courses applied to the M.S. degree can also be applied to the Ph.D. degree. Students who have taken similar graduate-level courses at another university may petition to apply these courses to the Ph.D. requirements. Ph.D. students who have served as teaching assistants, readers, or tutors at another university may petition to apply this experience toward the teaching practicum requirement. The normative time for advancement to candidacy is three years (two for students who entered with a master's degree). The normative time for completion of the Ph.D. is six years (five for students who entered with a master's degree), and the maximum time permitted is seven years.

 

 

Core Courses

NetSys 201 (Computer and Communication Networks) [cross listed with EECS 248A and CS 232]
NetSys 202 (Networking Laboratory) [cross listed with CS 233]
3 units of NetSys 295 (Networked Systems Seminar) in Fall, Winter and Spring of the same academic year

 

Breadth Courses

Computer Science & Engineering
Breadth Courses


CS 202 (Applied Cryptography)
CS 222 (Principles of Data Management)
CS 250A (Computer Systems Architecture) or EECS 213 (Computer Architecture)
CS 260 (Fundamentals of the Design and Analysis of Algorithms) or EECS 215 (Design and Analysis of Algorithms)
CS 261 (Data Structures)
CS 265 (Graph Algorithms)
CS 278 (Probability Models) or EECS 240 (Random Processes)
EECS 211 (Advanced System Software)
EECS 247 (Information Storage)
EECS 260A (Linear Systems I)

 

Management and Applications of Technology
Breadth Courses


CS 204 (Usable Security and Privacy) [cross listed with Inf 237]
Education 131 (Educational Technology)
Inf 251 (Computer-Supported Cooperative Work)
Inf 261 (Social Analysis of Computing)
Inf 265 (Theories of the Information Society)
Inf 267 (Digital Media and Society)
Inf 269 (Computer Law)
MGMT 178 (Management of Information Technology)
Planning, Policy, and Design 106 (Technology and Economic Development)
Sociology 212 (Network Theory)

 

Concentration Courses

Networks Concentration

NetSys 210 (Advanced Networks) [cross-listed with CS 234]
NetSys 230 (Wireless and Mobile Networking) [cross-listed with CS 236]
NetSys 240 (Network and Distributed System Security) [cross-listed with CS 203]
CS 230 (Distributed Computer Systems)
CS 244 (Introduction to Embedded and Ubiquitous Systems)
 

Performance Concentration

CS 206 (Principles of Scientific Computing)
CS 268 (Introduction to Optimization)
CS 271 (Introduction to Artificial Intelligence)
CS 273A (Machine Learning)
EECS 261A (Linear Optimization Methods)
CEE 221A (Transportation Systems Analysis I)
CEE 221B (Transportation Systems Analysis II)
CEE 228A (Urban Transportation Networks I)
CEE 228B (Urban Transportation Networks II)
MAE 206 (Nonlinear Optimization Methods)
Econ 116A (Game Theory)
Sociology 280 (Analysis of Social Network Data)

 

Middleware Concentration

NetSys 260 (Middleware for Networked and Distributed Systems) [cross-listed with CS 237]
CS 212 (Multimedia Systems and Applications)
CS 221 (Information Retrieval, Filtering, and Classification) [cross-listed with Inf 225]
CS 223 (Transaction Processing and Distributed Data Management)
CS 238 (Advanced Operating Systems)
EECS 203A (Digital Image Processing)
EECS 219 (Distributed Software Architecture and Design)
EECS 223 (Real-time Computer Systems)
EECS 224 (High-Performance Computing)

 

Communications Concentration

EECS 241A (Digital Communications I)
EECS 241B (Digital Communications II)
EECS 242 (Information Theory)
EECS 243 (Error Correcting Codes)
EECS 244 (Wireless Communications)
EECS 245 (Space-Time Coding)
EECS 250 (Digital Signal Processing I)
EECS 251A (Detection & Estimation, and Theory I)
EECS 251B(Detection & Estimation, and Theory II)

 

Students who entered the program before Fall 2018 can follow these requirements or the prior requirements (which can be found here).

 

 

last modified July 19, 2018
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