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Related lectures (27)

Linear Algebra Review

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

SVD: Singular Value Decomposition

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

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.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Singular Value Decomposition: Image Compression and Applications

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

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Document Classification: Features and Models

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Matrix Factorization: Optimization and Evaluation Log in to Mediaspace to watch this video

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

Optimization in Machine Learning Log in to Mediaspace to watch this video

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

Recommender Systems: Matrix Factorization & Evaluation Log in to Mediaspace to watch this video

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

Recommender Systems Log in to Mediaspace to watch this video

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

Linear Systems: Diagonal and Triangular Matrices, LU Factorization Log in to Mediaspace to watch this video

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

Unsupervised Learning: Movie Recommendation Log in to Mediaspace to watch this video

Covers unsupervised learning for movie recommendation using singular value decomposition.

Recommender Systems: MovieLens Dataset Log in to Mediaspace to watch this video

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

Matrix Inversion Log in to Mediaspace to watch this video

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

Characterization of Invertible Matrices Log in to Mediaspace to watch this video

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

Latent Semantic Indexing: Concepts and Applications Log in to Mediaspace to watch this video

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Singular Value Decomposition: Applications and Interpretation Log in to Mediaspace to watch this video

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

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