Lecture, two hours; laboratory, four hours. Requisites: courses 1A, 1B, 1C (or 1AH, 1BH, 1CH), Mathematics 32A, 33A. Preparation: experience in programming using Python. Project-based course designed for students with no previous experience in machine learning to learn about methods and algorithms in machine learning and their application to scientific problems in physical sciences. Students develop experience in compilation, analysis, and cleaning of data. Machine learning topics include classification, regression, dimensionality reduction, clustering, and kernel methods. P/NP or letter grading.