The minor in Computational Artificial Intelligence (mCAI) is designed to provide WashU students with the skills to pursue research and careers that require competence with emerging trends in artificial intelligence.
mCAI Admissions
Admissions to the mCAI minor is based on the candidate's completion of the prerequisites listed below. Note: Only a single course may be completed via transfer.
Program Prerequisites
Mathematics Prerequisites
Students must complete the following three courses:
| Code | Title | Units |
|---|---|---|
| MATH 2130 | Calculus III | 3 |
| ENGR 3180 | Engineering Mathematics A | 3 |
| or ESE 2180 | Linear Algebra and Component Analysis | |
| or MATH 3300 | Matrix Algebra | |
| or MATH 4301 | Linear Algebra | |
| ESE 3260 | Probability and Statistics for Engineering | 3 |
| or ENGR 3280 | Engineering Statistics With Probability | |
| or SDS 3020 | Elementary to Intermediate Statistics and Data Analysis | |
| or SDS 3030 | Statistics for Data Science I | |
| or SDS 5212 | Statistics for Data Science I | |
| Total Units | 9 | |
Computing Prerequisites
| Code | Title | Units |
|---|---|---|
| CSE 2407 | Data Structures and Algorithms * | 3 |
- *
CSE 2407 has a prerequisite of CSE 1301 Introduction to Computer Science or equivalent experience.
Program Requirements
Foundation (Core) Courses: 6 units required
| Code | Title | Units |
|---|---|---|
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| or ESE 4170 | Introduction to Machine Learning and Pattern Classification | |
| or SDS 4430 | Statistical Learning | |
| Total Units | 6 | |
Electives: 9 units required
Students must complete any three of the following**:
| Code | Title | Units |
|---|---|---|
| CSE 3101 | Introduction to Intelligent Agents Using Science Fiction | 3 |
| CSE 3104 | Data Manipulation and Management | 3 |
| CSE 4061 | Text Mining | 3 |
| CSE 4101 | AI and Society | 3 |
| CSE 4109 | Introduction to AI for Health | 3 |
| CSE 5100 | Deep Reinforcement Learning | 3 |
| CSE 5103 | Theory of Artificial Intelligence and Machine Learning | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5106 | Multi-Agent Systems | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5108 | Human-In-The-Loop Computation | 3 |
| CSE 5109 | Advanced Machine Learning | 3 |
| CSE 5180 | Heuristic Search and Constraint Processing | 3 |
| CSE 5270 | Natural Language Processing | 3 |
| CSE 5271 | Data-Driven Privacy and Security | 3 |
| CSE 5370 | Trustworthy Autonomy | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5500 | Mobile Robotics | 3 |
| CSE 5505 | Adversarial AI | 3 |
| CSE 5509 | Computer Vision | 3 |
| CSE 5610 | Large Language Models | 3 |
- **
Students also have the option to use a 3-unit, relevant, domain-specific course from another department. Examples include ECON 6140 Machine Learning and Data Science in Economics, MEMS 5205 Machine Learning Applications in Mechanical Engineering, PHYSICS 4080 Artificial Intelligence and Machine Learning Methods With Applications to Physics, and so on.
Additional Information
All courses used for the mCAI minor must be taken for a grade, and the student must earn a C– or better.