Pre Approved M.S. Electives
Pre Approved Electives (please note courses are not offered every quarter or every year)
Econ 111A,B,C: Intermediate Accounting (with permission of instructor)
Econ 124: Machine Learning for Economists
Econ 188: Management in the Global Economy
Econ 211C: Ph.D. Time Series (with permission of instructor)
Econ 231: International Financial Markets
Econ 234: Financial Institutions and Markets
Econ 235: Corporate Finance
Econ 238: Market Design: Theory and Pragmatics
Econ 259B: Public Policy Analysis
Applied Mathematics
AM 216: Stochastic Differential Equations
Computer Science & Engineering
CSE 20: Introduction to Programming in Python*
CSE 101: Algorithms and Abstract Data Types
CSE 102: Introduction to Analysis of Algorithms
CSE 111: Advanced Programming (with permission of instructor)
CSE 142: Machine Learning (with permission of instructor)
CSE 182: Introduction to Database Management Systems
CSE 201: Analysis of Algorithms (with permission of instructor)
CSE 202: Combinatorial Algorithms (with permission of instructor)
CSE 243: Data Mining (with permission of instructor)
CSE 270B: Management of Technology II (with permission of instructor)
CSE 271: E-Business Technology and Strategy (with permission of instructor)
CSE 272: Information Retrieval (with permission of instructor)
CSE 277: Random Process Models in Engineering (with permission of instructor)
*As it is a lower-division course, CSE 20, does not count toward the 35 credits required by the university to obtain a masters degree. However, since it broadens the skill-set of students in the program, we allow for it as a masters elective to satisfy department requirements. Before enrolling in this course, students should take care to ensure that they will have 35 eligible credits for graduation.
Environmental Studies
ENVS 140: National Environmental Policy
Statistics
STAT 206: Applied Bayesian Statistic
STAT 206B: Intermediate Bayesian Inference
STAT 207: Intermediate Bayesian Statistical Modeling
STAT 208: Linear Statistical Models
STAT 226: Spatial Statistics
