ECE 449
ECE 449 - Machine Learning
Spring 2021
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Machine Learning | CS446 | P3 | 31421 | ONL | 3 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | CS446 | P4 | 39433 | ONL | 4 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | CS446 | R3 | 68039 | OLC | 3 | 1530 - 1645 | T R | Alexander Schwing | |
| Machine Learning | CS446 | R4 | 68040 | OLC | 4 | 1530 - 1645 | T R | Alexander Schwing | |
| Machine Learning | ECE449 | P3 | 70856 | ONL | 3 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | ECE449 | P4 | 70857 | ONL | 4 | 1530 - 1645 | T R | Matus Jan Telgarsky | |
| Machine Learning | ECE449 | R3 | 72808 | OLC | 3 | 1530 - 1645 | T R | Alexander Schwing | |
| Machine Learning | ECE449 | R4 | 72809 | OLC | 4 | 1530 - 1645 | T R | Alexander Schwing |
See full schedule from Course Explorer
Official Description
Course Information: Same as CS 446. See CS 446.
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
Last updated
1/22/2020