CS-421 / 4 crédits

Enseignant: Käser Jacober Tanja Christina

Langue: Anglais


Summary

Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will cover the core methods of user modeling and personalization, with a focus on educational data.

Content

Learning Prerequisites

Required courses

The student must have passed a course in probability and statistics and a course including a programming project

Recommended courses

- CS-433 Machine learning or

- CS-233a / CS-233b Introduction to machine learning

Important concepts to start the course

Probability and statistics, basic machine learning knowledge, algorithms and programming

Learning Outcomes

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

  • Explain the main machine learning approaches to personalization, describe their advantages and disadvantages and explain the differences between them
  • Implement algorithms for these machine learning models
  • Apply them to real-world data
  • Assess / Evaluate their performance
  • Explain and understand the fundamental theory underlying the presented machine learning models

Teaching methods

- Lectures

- Weekly lab sessions

- Course project

Expected student activities

- Attend the lectures

- Attend the lab sessions and work on the homework assignments

- Project work

Assessment methods

- Project (40%)

- Homework (20%)

- Final exam (40%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Dans les plans d'études

  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Ecrit (session d'été)
  • Matière examinée: Machine learning for behavioral data
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
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