Machine Learning | CS 5350/6350, University of Utah

Comprehensive course covering supervised and unsupervised learning, decision trees, online learning, and more. Develop a strong foundation in machine learning and its applications.

Introduction

This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include several algorithms for supervised and unsupervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, and neural networks.

Highlights

• Broad theoretical and practical understanding of machine learning paradigms and algorithms
• Ability to implement learning algorithms
• Ability to identify where machine learning can be applied and make the most appropriate decisions

Recommendation

This course is suitable for students who are interested in developing a strong foundation in machine learning and its applications. It provides a comprehensive overview of the field and equips students with the necessary skills to apply machine learning techniques in various domains.