Course Websites
ECE 449 - Machine Learning
Last offered Spring 2021
Official Description
Course Information: Same as CS 446. See CS 446.
Related Faculty
Course Director
Goals
The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas, (1) discriminative models, (2) generative models, and (3) reinforcement learning models. In particular we will cover the following: linear regression, logistic regression, support vector machines, deep nets, structured methods, learning theory, kMeans, Gaussian mixtures, expectation maximization, VAEs, GANs, Markov decision processes, Q-learning and Reinforce.
Topics
- linear regression,
- logistic regression,
- support vector machines,
- deep nets,
- structured methods,
- learning theory basics,
- kMeans,
- Gaussian mixtures,
- expectation maximization,
- VAEs,
- GANs,
- Markov decision processes,
- Q-learning
- Reinforce
Topical Prerequisites
- Linear Algebra
- Probability
- Multivariate Calculus
- Python
Texts
No text.
ABET Category
Engineering Science: 1 credit
| Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|
| Machine Learning | P3 | 70856 | ONL | 3 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | P4 | 70857 | ONL | 4 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | R3 | 72808 | OLC | 3 | 1530 - 1645 | T R | Alexander Schwing | |
| Machine Learning | R4 | 72809 | OLC | 4 | 1530 - 1645 | T R | Alexander Schwing |