PHYS-467 / 4 credits

Teacher: Zdeborová Lenka

Language: English


Summary

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised.

Content

Learning Prerequisites

Important concepts to start the course

Basic notions in probability, analysis and basic familiarity with programming. Some notions of statistical physics will be used to support this lecture.

 

Learning Outcomes

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

  • Use basic tools for data analysis and for learning from data
  • Explain basic principles of data analysis and learning from data
  • List and explain machine learning tools suited for a given problem.

Teaching methods

2h of lecture + 2h of excercise (exercise mostly with a computer)

Assessment methods

Final written exam counting for 50% and several graded homeworks during the semester counting for the other 50%.

 

 

Resources

Bibliography

A high-bias, low-variance introduction to Machine Learning for physicists. Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab, https://arxiv.org/abs/1803.08823.

 

Text book "Information Theory, Inference, and Learning Algorithms" by David MacKay.

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for physicists
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for physicists
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for physicists
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for physicists
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9    MEB331
9-10    
10-11    MEB331
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 8h - 10h: Lecture MEB331

Friday, 10h - 12h: Exercise, TP MEB331