Knowledge Tracing: Student Learning and Latent Variables

Related lectures (32)

Page 3 of 4

Covers the theoretical foundations of choice theory, decision makers, alternatives, attributes, decision rules, utility, and behavioral assumptions.

Explores choice models to predict consumer behavior and market shares.

Covers variational inference and neural networks for classification tasks.

Delves into human decision-making beyond rationality, exploring concepts like the decoy effect.

Explores decision models in computational neuroscience, focusing on competitive dynamics, perceptual decision making, and the problem of free will.

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Discusses data visualization techniques, emphasizing their applications and principles for effective communication of complex information.

Focuses on GPS data collection, statistical analysis, and visualization techniques for geomatics exercises.

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