Machine Learning concerns the design and analysis of algorithms that allow computers to learn based on data. Over the last two decades, Machine Learning has emerged as a powerful tool in Artificial Intelligence applications including vision, natural language processing, speech and audio processing, robotics, and data mining. This course will focus on Graphical Models, Neural Networks, Expectation Maximization, Nearest-Neighbors and Spectral Clustering. Through examination of these algorithms, we will discuss the mathematical underpinnings of optimization and generalization. Previous coursework in linear algebra and calculus is highly recommended. Comfort with statistics and/or probability will be helpful but not necessary.