Course Machine Learning (COMPSCI 589, Fall 2022)

Time Tuesday + Thursday / 4:00PM - 5:15PM

Course Final Mon. Dec. 19, 3:30PM-5:30PM

Course Description This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results.

Textbook The course has no mandatory textbook. There will be optional readings from :

• The Elements of Statistical Learning by Hastie, Tibshirani and Friedman
• An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani

Grading Scheme

• Homework: 40%
• Final: 30%
• Quizzes: 25%
• Participation: 5%

Homework There will be 6 homework assignments, all of which count equally towards the course grade:

• Prerequisites
• Regression
• Classification and model selection
• Kernel methods
• Probabilistic Learning