Machine Intelligence

Course Description

This course is a hands-on introduction to machine learning. The main question addressed is: How can we design good computer algorithms that improve automatically through experience (e.g. similar to the way humans learn)? Multiple machine learning models are examined. The goal of the course is that students begin to understand some of the issues and challenges facing machine learning while being exposed to the pragmatics of implementing machine learning systems. We will cover topics such as decision trees, artificial neural nets, deep learning, Bayesian learning, k-means clustering, and genetic algorithms.

Course assumes familiarity with Python programming, linear algebra, basic probability theory.

Course Prerequisites: Math 120, 130, and any CS 2xx