Lectures related to Recommender Systems: Matrix Factorization & Evaluation

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Recommender Systems: Matrix Factorization

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

Linear Systems: Chapters 4, 5, 6

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

Optimization in Machine Learning

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

Recommender Systems: MovieLens Dataset

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

Linear Algebra Review

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

Recommender Systems

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

Characterization of Invertible Matrices

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

Determinant, Area, and Volume

Explores area determination, linear applications, matrix operations, and vector spaces.

Singular Value Decomposition: Image Compression and Applications

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

Linear Systems: Diagonal and Triangular Matrices, LU Factorization

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Singular Value Decomposition: Applications and Interpretation

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Linear systems resolution

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

Recommender Systems: Part 1

Introduces recommender systems, collaborative filtering, content-based recommendation, similarity metrics, and matrix factorization.

Matrix Factorization: LU Decomposition

Explores LU decomposition for matrix factorization and solving linear systems.

SVD: Singular Value Decomposition

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

Semi-Definite Programming

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

Stochastic Optimization and Adaptive Gradient Methods

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

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.

Matrix Factorizations: LU Decomposition

Introduces LU decomposition for efficient linear equation solving using matrix factorization.