CS 334 - Machine Learning

Course Overview

Machine learning impacts many applications including the sciences (e.g., predicting genome-protein interactions, detecting tumors, personalized medicine) and consumer products (e.g., Amazon’s Alexa, Microsoft Kinect, Netflix). In this course, students will cover the underpinnings, algorithms, and practices that enable a computer to learn. Emphasis will be on fundamental theory and algorithms in statistical machine learning, and approaches to applying machine learning in a variety of domains. Students will also obtain practical experience applying standard machine learning methods to solve a variety of problems.

Prerequisites

• CS 170/171/224/253
• MATH 221

Textbook

• Required: An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
• Supplemental: A Course in Machine Learning, by Hal Daumé III
• Supplemental: The Elements of Statistical Learning, by Hastie, Tibshirani & Friedman

Details

For detailed syllabus information, please see the Canvas course website.