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The University of Texas at Austin

Diversity Courses

The courses that are currently approved to fulfill the diversity requirement of both the Ph.D. and the Masters degree are shown below.

There are three Diversity areas: Theory, Systems, and Applications.

Ph.D. students must take one diversity course from each of five different threads, with no more than two threads in each of the three areas; threads are identified as numbered lines within each area. Additional Diversity courses may be taken as part of the Depth program.

Masters students must take at least one Diversity course from each of the three areas. Additional Diversity courses may be taken as part of the coursework for the Masters degree.

Theory
  1. CS 388G Algorithms: Techniques & Theory; CS 388R* Randomized Algorithms
  2. CS 388T Theory of Computation; CS 388C Combinatorics & Graph Theory; CS 388M* Communication Complexity
  3. CS 388P Parallel Algorithms; CS 388H* Cryptography
  4. CS 388L Introduction to Mathematical Logic; CS 389R Recursion & Induction I; CS 388S Formal Semantics & Verification
Systems
  1. CS 380L Advanced Operating Systems; CS 380N Systems Modeling
  2. CS 380D Distributed Computing I; CS 386C Dependable Computing Systems; CS 380P* Parallel Systems
  3. CS 386M Communication Networks; CS 396M Advanced Networking Protocols; CS 386W* Wireless Networking
  4. CS 386L Programming Languages; CS 380C Compilers
  5. CS 380S* Theory and Practice of Secure Systems; CS 386S Network Protocol Security
  6. CS 382M Advanced Computer Architecture
Applications
  1. CS 386D Database Systems (This course takes the place of courses CS 386 and CS 387H which will no longer be taught. Students who have taken these courses may still apply them toward their degree; however they cannot also get credit for CS 386D).
  2. CS 383C Numerical Anaylsis: Linear Algebra; CS 383D Numerical Analysis: Interpolation, Approximation, Quadrature,and Differential Equations; CS
  3. 384G Computer Graphics; CS 384R* Geometric Modeling & Visualization; CS
  4. 391L Machine Learning; CS 394N Neural Networks; CS 394R* Reinforcement Learning: Theory and Practice; CS 395T Data Mining: A Statistical Learning Perspective
  5. CS 381K Artificial Intelligence; CS 393R* Autonomous Robots; CS 393C* Agent-Based Electronic Commerce; CS 394F Knowledge Representation & Reasoning
  6. CS 394P Automatic Programming; CS 392F* Feature Oriented Programming
  7. CS 395T Algorithms for Computational Biology (Originally named Computational Biology)

*These courses were originally taught as topic courses (CS 395T). Students should be aware that they will not receive dual credit for retaking the course under the new course number.