CSCI 5770G - Machine Learning

This course will focus on both supervised and un-supervised learning methods, covering both theory and practice. The course is geared towards students who wish to develop a working knowledge of the recent advances in machine learning, and how these are applied in various domains. Machine learning deals with how to design computer programs that learn from “experience.” Residing at the intersection of computer science and statistics, machine learning aims to extract useful information from data (often referred to as the training data) and leverages this information to create computer models capable of carrying out useful, non-trivial tasks, such as designing cars that can drive on their own, filters for blocking junk email, diagnostics tools for disease discovery, etc. Students are expected to have prior background in linear algebra, calculus, probability and statistics, as well as experience in a high-level scripting language such as Python or MATLAB.

The course will cover the following topics:

Calculus, linear algebra and statistical foundations of machine learning
Non-parametric learning
Linear regression, Maximum Likelihood Estimation view of least regression
Logistic regression, softmax
Feed-forward perceptron networks
Autoencoders, Siamese networks
Recurrent neural networks, long short-term memory networks
Generative adversarial learning
Gaussian processes
Reinforcement learning
Bayesian learning

Credit hours: 3
Credit restriction(s): MITS 6800G