CS-233 / 6 credits

Teacher(s): Fua Pascal, Salzmann Mathieu

Language: English


Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Content

Learning Prerequisites

Required courses

Linear Algebra

Important concepts to start the course

  • Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD)
  • Multivariate calculus (derivatives w.r.t. vector and matrix variables)
  • Basic programming skills (labs will use Python).

Learning Outcomes

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

  • Define the following basic machine learning problems : regression, classification, clustering, dimensionality reduction
  • Explain the main differences between them
  • Derive the formulation of these machine learning models
  • Assess / Evaluate the main trade-offs such as overfitting, and computational cost vs accuracy
  • Implement machine learning methods on real-wolrd problems, and rigorously evaluate their performance using cross-validation

Teaching methods

  • Lectures
  • Pen-and-paper exercise sessions
  • Python lab with a mini project in groups of 3 students

Expected student activities

  • Attend lectures
  • Attend lab sessions
  • Work on the weekly theory and coding exercises

Assessment methods

  • Self-assessment via the solutions of the pen-and-paper exercises and coding labs
  • Two milestones for the mini-proejct (10% of the grade each)
  • Final exam (80% of the grade)

Supervision

Office hours No
Assistants Yes
Forum Yes

Resources

Moodle Link

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Introduction to machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Introduction to machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Introduction to machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Introduction to machine learning
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9 RLC E1 240   
9-10    
10-11 GRC001
CM013
CE1100
CE1101
CE1103
CM1120
   
11-12    
12-13     
13-14     
14-15     
15-16    GRA330
GRA331
GRB330
BS150
BS160
BS270
BS260
16-17    
17-18     
18-19     
19-20     
20-21     
21-22     

Tuesday, 8h - 10h: Lecture RLC E1 240

Tuesday, 10h - 12h: Exercise, TP GRC001
CM013
CE1100
CE1101
CE1103
CM1120

Friday, 15h - 17h: Project, other GRA330
GRA331
GRB330
BS150
BS160
BS270
BS260

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