MICRO-401 / 2 credits

Teacher(s): Billard Aude, Busch Baptiste

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 C/C++ or 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.

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

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 will be evaluated on the report and code handed out at the end of the course.

Prerequisite for

Students must know how to program either in Matlab or C/C++.

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     
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