CS 534 - 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 learn the fundamental theory and algorithms of machine learning. Students will also obtain practical experience applying standard machine learning methods to solve a variety of problems.

Prerequisites

• Undergraduate-level linear algebra
• Undergraduate-level probability
• Undergraduate-level algorithms
• Programming ability in Python

Textbook

• Required: The Elements of Statistical Learning, by Hastie, Tibshirani & Friedman
• Supplemental: Machine Learning: a Probabilistic Perspective, by Kevin Murphy
• Supplemental: Understanding Machine Learning: From Theory to Algorithms, by Shalev-Shwartz & Ben-David
• Supplemental: A Course in Machine Learning, by Hal Daumé III

Details

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