Foundations of Machine Learning

Foundations of Machine Learning is a comprehensive course that introduces the fundamental concepts and methods of machine learning. The course covers both supervised and unsupervised learning, as well as a variety of machine learning algorithms, including linear regression, logistic regression, decision trees, support vector machines, CNN and ensemble methods.

The course also covers the theoretical foundations of machine learning, including the PAC learning model, Rademacher complexity, and VC dimension. This allows students to understand how machine learning algorithms work and to develop a deeper understanding of their limitations.

Prerequisites

Students should have a strong foundation in linear algebra, probability, and statistics. It is also helpful to have some experience with programming in Python or another similar language.

Course Objectives

Upon completion of this course, students will be able to:

Understand the basic concepts and methods of machine learning
Apply machine learning algorithms to solve real-world problems
Evaluate the performance of machine learning models
Understand the theoretical foundations of machine learning