Welcome to CS243:

Topics in Machine Learning

Date and Time: Wednesdays, 3:00-5:00pm in CIT Room 506
Instructors: Tom Dean with help from Kee-Eung Kim and Luis Ortiz

Index:

Introduction
Syllabus and Handouts
Textbook and Supplementary Readings

Introduction

This course covers machine learning from the AI perspective. We consider different learning problems, including concept learning, clustering, and reinforcement learning. For each learning problem, we investigate a variety of proposed solutions, including those from outside of mainstream AI, such as artificial neural networks and standard statistical methods. Students are expected to design an empirical learning experiment involving replication of published results, comparison of existing algorithms, or comparison of an original algorithm with existing algorithms, then carry out and report on the results of the experiment. This course is aimed at students interested in learning about the basic practical and theoretical issues in machine learning.


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