Students will acquire the core engineering mathematics essential for conducting research in communications and artificial intelligence.

- Introduction to Linear Algebra, Wellesley-Cambridge Press, Gilbert Strang
- Convex Optimization, Cambridge University Press, Stephen Boyd and Lieven Vandenberghe

Part I: Math
1. Linear Algebra (2 weeks)
2. Analytic Geometry (1 week)
3. Matrix Decomposition (1 week)
4. Vector Calculus (1 week)
5. Probability and Distributions (2 weeks)
6. Optimization (2 weeks)

Part II: 4 Basic Machine Learning Problems
1. When Models Meet Data (1 week)
2. Dimensionality Reduction with Principal Component Analysis (1 week)
3. Density Estimation with Gaussian Mixture Models (1 week)
4. Classification with Support Vector Machines (1 week)

Lectures

This is a fourth-year undergraduate course; however, third-year students and master’s-level graduate students are also eligible to enroll.