Coursebooks

Advanced machine learning

MICRO-570

Lecturer(s) :

Billard Aude

Language:

English

Summary

This course will present some of the core advanced methods in the field for structure discovery, classification and non-linear regression. This is an advanced class in Machine Learning; hence, students are expected to have some background in the field.

Content

The class will be accompanied by practical session on computer, using the mldemos software (http://mldemos.epfl.ch) that encompasses more than 30 state of the art algorithms.

Keywords

Machine learning, statistics

Learning Prerequisites

Required courses

Probability & Statistics, Linear Algebra

Recommended courses

Machine Learning, Pattern Recognition

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

 

Learning Outcomes

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

Transversal skills

Teaching methods

Ex-cathedra lectures, exercises, computer-based practical sessions

Expected student activities

Each week, students should read the selected chapters of the Lecture Notes prior to class.

Students must attend the computer-based practice session and prepare regular reports that are graded.

Assessment methods

50% personal work during semester, 50% oral exam

 

Resources

Ressources en bibliothèque
Notes/Handbook

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

Prerequisite for

Students must be knowledgeable about machine learning and have taken a course in the area either at EPFL or elsewhere. Relevant courses at EPFL are:

Applied Machine Learning - MICRO-455

Pattern Classification and Machine Learning: CS-433

Data Analysis and Model Classification - EE-516

In the programs

    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2020-2021, Master semester 2
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2020-2021, Master semester 4
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      3 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
    • Project
      1 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     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Lecture in French
  • Lecture in English
  • Lecture in German