Machine Learning | Carnegie Mellon University

Gain a deep understanding of the theoretical and practical aspects of machine learning, including Bayesian networks, decision tree learning, and Support Vector Machines.

Introduction

This course covers the theory and practical algorithms for machine learning from a variety of perspectives. It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor.

Highlights

• Covers a wide range of machine learning topics, including Bayesian networks, decision tree learning, Support Vector Machines, and more
• Includes hands-on programming assignments to experiment with various learning algorithms
• Designed to provide a thorough grounding in the methodologies, technologies, mathematics, and algorithms needed for machine learning research

Recommendation

This course is recommended for graduate-level students who are interested in gaining a deep understanding of the theoretical and practical aspects of machine learning. The course assumes a strong background in probability, linear algebra, statistics, and algorithms, making it suitable for students with a strong numerate background.