Lecture

Recommender Systems: Part 1

Related lectures (24)

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

Optimization in Machine Learning

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

Matrix Factorization: Optimization and Evaluation

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

Recommender Systems: Matrix Factorization & Evaluation

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

Recommender Systems

Explores the evolution and impact of recommender systems, covering information retrieval, collaborative filtering, and different recommendation algorithms.

Stochastic Optimization and Adaptive Gradient Methods

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

Linear Algebra Review

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

Recommender Systems: Basics and Techniques

Covers collaborative filtering and content-based methods for recommender systems, addressing cold start problems and making predictions.

Recommender Systems: MovieLens Dataset

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

Recommender Systems: Personalized Recommendations for Users

Delves into challenges of content personalization, focusing on web recommendations and personalized newsletters.

Recommender Systems: Matrix Factorization

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

Recommender Systems: Overview and Methods

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Matrix Factorizations: LU Decomposition

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

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Matrix Factorization: LU Decomposition

Explores LU decomposition for matrix factorization and solving linear systems.

Decomposition Spectral: Symmetric Matrices

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Direct Methods for Solving Linear Equations

Explores direct methods for solving linear equations and the impact of errors on solutions and matrix properties.

Singular Value Decomposition

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

SVD: Singular Value Decomposition

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

Matrix Inversion

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.