Matrix Factorization: Optimization and Evaluation

Related lectures (24)

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

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Explores the link between linear systems and optimization through elimination and LU decomposition.

Explores differentiable ranking and sorting techniques for machine learning applications.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers implementing recommender systems using the MovieLens dataset and evaluating them with RMSE and MAE metrics.

Covers semi-definite programming and optimization over positive semidefinite cones.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.