Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.

Content

Learning Prerequisites

Recommended courses

• Analysis I, II, III
• Linear Algebra
• Machine learning
• Probability
• Algorithms (CS-250)

Learning Outcomes

By the end of the course, the student must be able to:

• Explain the framework of PAC learning
• Explain the importance basic concepts such as VC dimension and non-uniform learnability
• Describe basic facts about representation of functions by neural networks
• Describe recent results on specific topics e.g., graphical mdoel learning, matrix and tensor factorization, learning mixture models

Teaching methods

• Lectures
• Exercises

Expected student activities

• Attend lectures
• Attend exercises sessions and do the homework

Assessment methods

Final exam and graded homeworks

Supervision

Resources

Moodle Link

• https://go.epfl.ch/CS-526