EE-411 / 4 credits

Teacher: Krzakala Florent Gérard

Language: English


Summary

This is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternate, between mathematical theoretical foundations and practical computational aspects in python.

Content

Keywords

Statisitics, Supervised and unsupervised learning

Learning Prerequisites

Required courses

A course in basic probability theory.

 

 

 

 

 

 

Recommended courses

linear algebra and statistics.

Python

 

Important concepts to start the course

Students should be familiar with basic concepts of probability theory, calculus and linear algebra, and be familiar with python.

 

 

 

Learning Outcomes

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

  • Formulate statistical models and apply them to statistical learning
  • Apply machine learning technics to data science problems
  • Solve concrete data science problems
  • Explain and understand the fundamental principle of learning theory

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Fundamentals of inference and learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Fundamentals of inference and learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9  GRB330  
9-10    
10-11 GRB330   
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Tuesday, 10h - 12h: Lecture GRB330

Wednesday, 8h - 10h: Project, other GRB330