Machine Learning: from Theory to Algorithms

Catalog description

Data science techniques and tools are all around us. Machine learning is a term used across many different disciplines, and often people use machine learning tools without a thorough understanding of how and why the tools work. This course will provide a foundation of machine learning grounded in the mathematical models behind the techniques. We will cover the theory, computational methods, and visualization inherent in the application of machine learning models. Students will learn the statistical learning framework, common assumptions in the data generation process, the mathematics behind machine learning models, including supervised and unsupervised techniques, as well as how to implement machine learning models in Python from scratch. Strong python skills (preferably object-oriented programing) are required. All DSI and CS ScM students will receive overrides as this is a required course for them.