Introduction to Machine Learning

Provides a broad introduction to different machine learning paradigms and algorithms, providing a foundation for further study or independent work in machine learning, artificial intelligence, and data science. Topics include linear models for classification and regression, support vector machines, neural networks, clustering, principal components analysis, Markov decision processed, planning, and reinforcement learning. The goals of this course are three-fold: to understand the landscape of ML, how to compute the mathematics behind techniques, and how to use Python and relevant libraries to implement and use various methods.