110 http://isa.epfl.ch/imoniteur_ISAP/ itffichecours.txt?path=EPFL.Etu.FicheCours.HeaderEN itffichecours.txt?path=EPFL.Etu.FicheCours.GlobalNav_js en 2026 946228 6683147 946228 1866895046 1866895046 0 0 Communication Systems SC IC Faculté informatique et communications School of Computer and Communication Sciences !itffichecours.htm?ww_i_matiere=2717976351&ww_x_anneeAcad=1866895046&ww_i_section=946228&ww_i_niveau=6683147 1696552884 1696552963 2020-2021 COURSE BOOKLET CS-421 Machine learning for behavioral data Machine learning for behavioral data Lecturers Käser Tanja Language of teaching English Cursus Communication Systems - master program(2020-2021, SC - Master EPFL 2019)
Communication Systems - master program(2020-2021, SC - Master EPFL 2020)
Computer Science - Cybersecurity(2020-2021, IN-CS - Master 2019)
Computer Science - Cybersecurity(2020-2021, IN-CS - Master 2020)
Computer Science(2020-2021, IN - Master 2019)
Computer Science(2020-2021, IN - Master 2020)
Data Science(2020-2021, SC_DS - Master 2019)
Data Science(2020-2021, SC_DS - Master 2020)
SemesterMand.OptionFilières
MA2, MA4x
 Credits 4 Session Summer Workload 120h Exam Written Number of positions Semester Spring Weeks 14 Hours 4 weekly
Courses 2 weekly
Project 2 weekly
Study planSemesterMand.OptionFilières
Cyber security 2020-21MA2x
Cyber security 2020-21MA4x
Data Science 2020-21MA2x
Data Science 2020-21MA4x
Informatique 2020-21MA2x
Informatique 2020-21MA4x
Systèmes de communication 2020-21MA2x
Systèmes de communication 2020-21MA4x
Cursus Sem. Type
Computer science MA2, MA4 Opt.
Cybersecurity MA2, MA4 Opt.
Data Science MA2, MA4 Opt.
SC master EPFL MA2, MA4 Opt.
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

The users of computer environments such as intelligent tutoring systems, interactive games, and web services are often very heterogeneous and therefore it is important to adapt to their specific needs and preferences.

This course will cover the core methods of adaptation and personalization, with a focus on educational data. Specifically we will discuss approaches to the task of accurately modeling and predicting human behavior within a computer environment. Furthermore, we we will also discuss data mining techniques with the goal to gain insights into human behavior. We will cover the theories and methodologies underlying the current approaches and then also look into the most recent developments in the field.

  1. ‘Cycle’ of adaptation : representation, prediction, intervention (e.g. recommendation)
  2. Data Processing and Interpretation (missing data, feature transformations, distribution fitting)
  3. Performance evaluation (cross-validation, error measures, statistical significance, overfitting)
  4. Representation & Prediction (probabilistic graphical models, recurrent neural networks, logistic models, clustering-classification approaches)
  5. Recommendation (collaborative filtering, content-based recommendations, multi-armed bandits)
  6. Stealth Assessment (seemless detection of user traits)
  7. Multimodal analytics (represent & analyze data from non-traditional sources. i.e. sensors, classroom analytics, human-robot interaction) 
 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 Informatique, 2020-2021, Master semestre 2 Computer Science, 2020-2021, Master semester 2 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 2 MA2 ETE Master semester 2 MA2 Informatique IN Computer Science IN
Informatique IN Computer Science IN
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Informatique, 2020-2021, Master semestre 4 Computer Science, 2020-2021, Master semester 4 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 4 MA4 ETE Master semester 4 MA4 Informatique IN Computer Science IN
Informatique IN Computer Science IN
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Systèmes de communication - master, 2020-2021, Master semestre 2 Communication Systems - master program, 2020-2021, Master semester 2 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 2 MA2 ETE Master semester 2 MA2 Systèmes de communication - master SC_EPFL Communication Systems - master program SC_EPFL
Systèmes de communication SC Communication Systems SC
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Systèmes de communication - master, 2020-2021, Master semestre 4 Communication Systems - master program, 2020-2021, Master semester 4 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 4 MA4 ETE Master semester 4 MA4 Systèmes de communication - master SC_EPFL Communication Systems - master program SC_EPFL
Systèmes de communication SC Communication Systems SC
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Informatique - Cybersecurity, 2020-2021, Master semestre 2 Computer Science - Cybersecurity, 2020-2021, Master semester 2 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 2 MA2 ETE Master semester 2 MA2 Informatique - Cybersecurity IN_CS Computer Science - Cybersecurity IN_CS
Informatique IN Computer Science IN
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Informatique - Cybersecurity, 2020-2021, Master semestre 4 Computer Science - Cybersecurity, 2020-2021, Master semester 4 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 4 MA4 ETE Master semester 4 MA4 Informatique - Cybersecurity IN_CS Computer Science - Cybersecurity IN_CS
Informatique IN Computer Science IN
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Data Science, 2020-2021, Master semestre 2 Data Science, 2020-2021, Master semester 2 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 2 MA2 ETE Master semester 2 MA2 Data Science SC_DS Data Science SC_DS
Systèmes de communication SC Communication Systems SC
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
Data Science, 2020-2021, Master semestre 4 Data Science, 2020-2021, Master semester 4 Ecole polytechnique fédérale de Lausanne EPFL Ecole polytechnique fédérale de Lausanne EPFL 2020-2021 Master semestre 4 MA4 ETE Master semester 4 MA4 Data Science SC_DS Data Science SC_DS
Systèmes de communication SC Communication Systems SC
optionnel optional
2 Cours Courses Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2 Projet Project Heure(s) hebdo x 14 semaines Hour(s) per week x 14 weeks H hebdo H hebdo Käser Jacober Tanja Christina 321948
Systèmes de communication SC Communication Systems SC
2717976351 Machine learning for behavioral data Machine learning for behavioral data 4
Ecrit Written Crédit Credits 4 Session été Session summer ETE ETE
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