Machine Learning | Cornell University CS4780/5780

Comprehensive course covering a wide range of machine learning techniques, including classification, structured models, clustering, and recommender systems. Provides theoretical foundations and hands-on experimentation.

Introduction

Machine learning is concerned with the question of how to make computers learn from experience. This course will introduce the fundamental set of techniques and algorithms that constitute machine learning, ranging from classification methods like decision trees and support vector machines, over structured models like hidden Markov models, to clustering and matrix factorization methods for recommendation.

Highlights

• Covers a wide range of machine learning techniques, including instance-based learning, decision trees, linear rules, support vector machines, generative models, hidden Markov models, structured output prediction, statistical learning theory, online learning, clustering, and recommender systems.
• Provides a theoretical perspective by tying principles and approaches together.
• Includes hands-on experimentation with machine learning algorithms and methods.

Recommendation

This course is recommended for students with programming skills, basic knowledge of linear algebra, and probability theory. It is suitable for those interested in understanding the fundamental techniques and algorithms that constitute machine learning and their theoretical foundations.