Course

CS-421: Machine learning for behavioral data

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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.

Structure Discovery: Tracing Student Knowledge

Introduces Bayesian Knowledge Tracing, Additive Factors Model, and clustering algorithms for tracing student knowledge and discovering structures.

Recommender Systems

Explores the evolution and impact of recommender systems, covering information retrieval, collaborative filtering, and different recommendation algorithms.

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Advanced Structure Discovery: Distance Metrics and Time Series Data

Explores clustering algorithms, distance metrics, and time series data analysis techniques.

Advanced Student Modeling

Explores advanced student modeling techniques, neural networks, LSTM networks, and Deep Knowledge Tracing.

Affective Computing: Theories, Applications, and Predictive Models

Explores emotion theories, applications, and predictive models in affective computing, analyzing NSF funding trends, emotion impact on education and medicine, and emotion detection through physiological signals and visual data.

Multimodal Analytics: Human-Robot Interaction and Learning Processes

Delves into Human-Robot Interaction, student engagement, and learning analytics using multimodal data to enhance educational processes.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Data Exploration: Time Series Analysis

Explores data analysis techniques, emphasizing time series prediction in a flipped classroom setting.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Model Evaluation

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Knowledge Tracing

Delves into Bayesian Knowledge Tracing and Learning Curves, exploring the prediction of student knowledge over time and the importance of accurate performance measurement.

Deep Knowledge Tracing

Explores Deep Knowledge Tracing and its application in predicting student learning outcomes using neural networks and loss functions.

Deep Learning: Recurrent Neural Networks

Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.