# Coursework

The following is a summary of the university-level coursework I completed at UC Berkeley. This includes courses I self-studied, which generally meant going along with a past or current iteration of the course, watching lectures and completing assignments, without receiving a grade.

## Graduate Level Coursework

### Electrical Engineering

- EE 221A (Linear System Theory)
- EE 223 (Stochastic Systems: Estimation and Control)
- EE 225A (Statistical Signal Processing)
- EE 226A (Random Processes in Systems)
- EE 227C (Convex Optimization and Approximation)
- EE 229A (Information Theory)
- EE 290-002 (High-Dimensional Data Analysis with Low-Dimensional Models)
- EE 290-008 (Design of Societal Scale Systems: Games, Incentives, Adaptation and Learning)

### Computer Science

- CS 270 (Combinatorial Algorithms and Data Structures)
- CS 294-182 (Theoretical Foundations of Learning, Decisions, and Games)
- CS 294-214 (Efficient Algorithms and Computational Intractability in Statistics)
- CS 294-220 (Computational Learning Theory)

### Mathematics

- Math 202A (Introduction to Topology and Analysis I)
- Math 202B (Introduction to Topology and Analysis II)
- Math 206 (Functional Analysis)
- Math 214 (Differentiable Manifolds)
- Math 240 (Riemannian Geometry)
- Math 258 (Harmonic Analysis)
- Math 279 (Stochastic Partial Differential Equations)

### Statistics

- Stat 205A (Probability Theory)
- Stat 210A (Introduction to Theoretical Statistics I)
- Stat 210B (Introduction to Theoretical Statistics II)

## Undergraduate Level Coursework

### Electrical Engineering

- EE 16B (Designing Information Devices and Systems II)
- EE 126 (Probability and Random Processes)
- EE 127 (Optimization Models in Engineering)

### Computer Science

- CS 61A (The Structure and Interpretation of Computer Programs)
- CS 61B (Data Structures)
- CS 61C (Great Ideas of Computer Architecture)
- CS 70 (Discrete Mathematics and Probability Theory)
- CS 161 (Computer Security)
- CS 162 (Operating Systems and Systems Programming)
- CS 170 (Efficient Algorithms and Intractable Problems)
- CS 182 (Designing, Visualizing and Understanding Deep Neural Networks)
- CS 189 (Introduction to Machine Learning)