Course

CS-421: Machine learning for behavioral data

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.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-for-behavioral-data-CS-421

Moodle Page

http://go.epfl.ch/CS-421

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Summary

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-for-behavioral-data-CS-421

Moodle Page

http://go.epfl.ch/CS-421

About this result

Lectures in this course (26)

Introduction to ML for Behavioral Data

Covers machine learning approaches for personalization and their real-world application, emphasizing homework, projects, and feedback.

Data Handling: Problems and Distributions

Covers common data problems and important distributions, along with correlation and dependencies analysis.

Regression and Classification

Explores regression, classification, linear models, decision trees, and random forests in data analysis.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.

Knowledge Tracing: Student Learning and Latent Variables

Explores Bayesian Knowledge Tracing and latent variables for tracing student knowledge and making predictions.

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