Introduction to Machine Learning

Catalog description

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, neural networks / deep learning, statistical learning methods, unsupervised learning, large language models, and deep reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, and Occam's Razor. Programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning. 10-301 and 10-601 are identical. Undergraduates must register for 10-301 and graduate students must register for 10-601 . 10-301 is recommended for undergraduates who are not SCS majors. CMU may video record or photograph lectures and recitations of this course, and to make these available to other CMU students or the broader public via the internet or other means. To attend this course, you will need to sign an online Authorization and Agreement form granting CMU full use rights of the recordings without compensation. Before registering, you must review the full policy on the course website. Prerequisites: ( 15-122 Min. grade C or 15-121 Min. grade C) and ( 21-256 Min. grade C or 21-241 Min. grade C or 21-254 Min. grade C or 21-259 Min. grade C or 21-240 Min. grade C or 21-127 Min. grade C) and ( 36-220 Min. grade C or 36-217 Min. grade C or 21-325 Min. grade C or 36-219 Min. grade C or 36-218 Min. grade C or 15-359 Min. grade C…