Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, such as in face recognition, web search, targeted advertising, speech processing, and genetic analysis. It is currently at the forefront of research in artificial intelligence, and has been making rapid strides given the vast availability of data today. This course is a broad introduction to the field, covering the theoretical ideas behind widely used algorithms like decision trees, linear regression, support vector machines, and many more. We will also study practical applications of these algorithms to problems in a variety of domains, including vision, speech, language, medicine, and the social sciences.

Units: 1

Max Enrollment: 20

Prerequisites: CS 230 or CS 230P or CS 230X, or permission of the instructor.

Instructor: Tjaden, Yacoby

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Fall; Spring

Notes: Mandatory Credit/Non Credit.