Coursebooks

Machine learning for behavioral data

CS-421

Lecturer(s) :

Käser Jacober Tanja Christina

Language:

English

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:

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 work (50%)

- Final exam (50%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

In the programs

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     
Under construction
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German