Machine learning for behavioral data
CS-421 / 6 credits
Teacher:
Language: English
Remark: pas donné en 2023-24
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.
- `Cycle¿ of adaptation : representation, prediction, intervention (e.g. recommendation)
- Data Processing and Interpretation (missing data, feature transformations, distribution fitting)
- Performance evaluation (cross-validation, error measures, statistical significance, overfitting)
- Representation & Prediction (probabilistic graphical models, recurrent neural networks, logistic models, clustering-classification approaches)
- Recommendation (collaborative filtering, content-based recommendations, multi-armed bandits)
- Stealth Assessment (seemless detection of user traits)
- 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
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 disavantages and explain thedifferences 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 work (50%)
- Final exam (50%)
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Exam form: Written (summer session)
- Subject examined: Machine learning for behavioral data
- Lecture: 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: Machine learning for behavioral data
- Lecture: 2 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
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| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other