Lectures related to Matrix Factorization: Optimization and Evaluation

Recommender Systems: Matrix Factorization

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

Recommender Systems: Matrix Factorization & Evaluation

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

Optimization in Machine Learning

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

Linear Systems: Chapters 4, 5, 6

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

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.

Linear Algebra Review

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

Recommender Systems: MovieLens Dataset

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

Semi-Definite Programming

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

Singular Value Decomposition: Image Compression and Applications

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

Stochastic Optimization and Adaptive Gradient Methods

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

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Characterization of Invertible Matrices

Explores the properties of invertible matrices, including unique solutions and linear independence.

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Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

SVD: Singular Value Decomposition

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Linear systems resolution

Covers the resolution of linear systems and its link to optimization problems.

Recommender Systems

Explores recommender systems, collaborative filtering, content-based recommendations, similarity metrics, and advanced methods like matrix factorization.

Matrix Factorization: LU Decomposition

Explores LU decomposition for matrix factorization and solving linear systems.