MICRO-401 / 2 credits

Teacher: Billard Aude

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.


Summary

This is a practice-based course, where students program algorithms in machine learning and evaluate the performance of the algorithm thoroughly using real-world dataset.

Content

Keywords

Programming in matlab. Machine Learning. Statistics.

Learning Prerequisites

Required courses

Students must have taken a machine learning course or follow one during the same semester. This programming class is meant to complement the Applied Machine Learning course, but can also complement other machine learning courses given at EPFL.

Students have at their disposal videos presenting the theory of the pendant course Applied Machine Learning as supplementary material.

Recommended courses

Applied Machine Learning - MICRO-455

Pattern Classification and Machine Learning: CS-433

Data Analysis and Model Classification - EE-516

 

Important concepts to start the course

Basic notions in Machine Learning:

Supervised versus unsupervised learning

Classification, non-linear regression, clustering

Learning Outcomes

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

  • Produce code for steps of ML algorithms
  • Develop a reasoning process to transform an algorithm into programming code

Transversal skills

  • Use both general and domain specific IT resources and tools

Teaching methods

Computer-based practice session. Some short ex-cathedra lectures will be given at the beginning of the class.

Expected student activities

Attendance to all sessions is necessary to progress rapidly and benefit from assistants' support.

Assessment methods

The students are evaluated on pieces of code handed out regularly throughout the course.

Supervision

Office hours No
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Moodle Link

Videos

Prerequisite for

Students must know how to program in Matlab (or C which is close and then be ready to learn Matlab).

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning programming
  • Practical work: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9  GRB001
CM1111
CM1112
GRC002
  
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     

Wednesday, 8h - 10h: Exercise, TP GRB001
CM1111
CM1112
GRC002