Introduction to Machine Learning

12 units · Undergraduate

Fall and Spring: 12 units 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, statistical learning methods, unsupervised learning and 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. Technologies used in this course include Python, NumPy, PyTorch, and Google Colab. 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. (SCS majors should instead take 10-315 .) Prerequisites: ( 15-121 Min. grade C or 15-122 Min. grade C) and ( 21-127 Min. grade C or 21-256 Min. grade C or 21-254 Min. grade C or 21-259 Min. grade C or 21-241 Min. grade C or 21-240 Min. grade C) and ( 36-225 Min. grade C or 15-259 Min. grade C or 21-325 Min. grade C or 36-218 Min. grade C or 36-217 Min. grade C or 36-219 Min. grade C or 15-359 Min. grade C or 36-220 Min. grade C) Course Website: http://mlcourse.org