Applied Machine Learning

CS
129
Instructors
Ng, A. (PI)
Section Number
1
(Previously numbered CS 229A.) 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. 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. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as Math 51.
Grading
Letter or Credit/No Credit
Units
3-4
Undergraduate
Course Tags
Computational Policy - Electives
Computational Policy Analysis
Academic Year
Quarter
Spring
Section Days
Wednesday
Start Time
9:45 AM
End Time
11:45 AM