Description

Machine Learning is the study of how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information theory, and probability theory, among others. The course will explain how to build systems that learn and adapt using examples from real-world applications.
The class will be self-contained (i.e., I will not assume any previous knowledge); a review session on probability and information theory will precede those chapters in need of background knowledge. Main topics include linear discriminants, neural networks, decision trees, support vector machines, unsupervised learning, reinforcement learning, etc.