MICRO-455 / 4 credits

Teacher: Billard Aude

Language: English


Summary

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data

Content

Keywords

Machine Learning, Statistics

Learning Prerequisites

Required courses

Linear Algebra, Probability & Statistics

Important concepts to start the course

Linear Algebra: Eigenvalue and singular value decomposition

Statistics: Definitions of probability density function, marginal, likelihood, covariance, correlation

Optimization: Lagrange multipliers, gradient descent, local and global optima

 

Teaching methods

Ex-cathedra, exercises, computer-based practical sessions

Expected student activities

Students who are no longer up to date with the pre-requisites should work on these in parralel to taking the class.

Students are expected to attend the exercise sessions and the computer-based practice sessions. They should revise the class notes prior to going to practical session to be on top of the the theoretical material prior to applying it.

Assessment methods

Final written exam (100% grade), in-class assessment through a quiz (0% grade).

Resources

Notes/Handbook

Machine Learning Techniques, available at the Librairie Polytechnique. To be purchased before the class starts.

Prerequisite for

Advanced Machine Learning, spring semester

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Applied machine learning
  • Lecture: 4 Hour(s) per week x 14 weeks

Reference week

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