Learning theory

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
• Explain the importance basic concepts such as VC dimension, bias-variance tradeoff and double descent
• Describe recent results on specific topics e.g., 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