Coursebooks 2017-2018

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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

Supervision

Office hours No
Assistants Yes
Forum No

Resources

Ressources en bibliothèque
Notes/Handbook

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

Websites
Moodle Link

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

  • Energy Management and Sustainability, 2017-2018, Master semester 2
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Project
      1 Hour(s) per week x 14 weeks
  • Energy Management and Sustainability, 2017-2018, Master semester 4
    • Semester
       Spring
    • Exam form
       Oral
    • Credits
      4
    • Subject examined
      Advanced machine learning
    • Lecture
      2 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
      2 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
      2 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
      2 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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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German