This tutorial examines the design, implementation, and analysis of machine learning algorithms. Machine Learning is a branch of Artificial Intelligence that has as its aim the development of algorithms that will improve a system's performance. Improvement might involve acquiring new factual knowledge from data learning to perform a new task, or learning to perform an old task more efficiently or effectively. This tutorial will cover instances of three general categories of algorithms: supervised learning algorithms (including decision tree learning, support vector machines, and neural networks), unsupervised learning algorithms (including k-means and expectation maximization), and reinforcement learning algorithms (such as Q learning and temporal difference learning). It will also introduce methods for the evaluation of learning algorithms, as well as topics in computational learning theory. Format: tutorial. Evaluation will be based on presentations, problem sets, short programming exercises, empirical analyses of algorithms, and two exams. Prerequisites: Computer Science 136 and Mathematics/Statistics 251. Computer Science 256 is recommended but not required. Enrollment limit: 10 (expected: 10) Preference given to Computer Science majors.