Lectures related to Recommender system

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Explores query expansion methods, user relevance feedback, Rocchio algorithm, and practical considerations in expanding queries.

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

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.