Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, showing up in face recognition, web search, targeted advertising, speech processing, genetic analysis, and even Facebook's selection of posts to display. 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 support vector machines, neural networks, graphical models, decision trees, and many more. We will also study practical applications of these algorithms to problems in vision, speech, language, biology, and the social sciences.
Max Enrollment: 18
Prerequisites: CS 230 and either MATH 206 or MATH 220 or MATH 225.
Distribution Requirements: EC - Epistemology and Cognition; MM - Mathematical Modeling and Problem Solving
Typical Periods Offered: Fall and Spring
Semesters Offered this Academic Year: Fall
Notes: Mandatory credit/noncredit.