CS446, Spring2020, UIUC
Course description:
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.
Grading: 4 credit: Homework 16.6%, Scribe 16.6%, Midterm 33%, Final 33%
**Language:**Python, Pytorch
Content
- Lectures
- Homework 1-10 (hw6 is dropped)
- Practice Problems for mid&final and solutions
- Scribe: Lecture 30