Course Description You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. Prerequisites: Programming at the level of CS106B or 106X, probability theory at the level CS109 or STATS116 and basic linear algebra at the level of MATH51. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Enrollment is limited. Consent of instructor required.
Acknowledegment This webpage is using the code from Shuqui Qu and Ziang Xie who have built the CS229 webpage, special thanks to them.