Category
Dimensionality reduction
Related lectures (161)
Textual Data Analysis: Classification & Dimensionality Reduction
Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.
Dimensionality Reduction
Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Principal Component Analysis: Eigenfaces
Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.
Dimensionality Reduction: PCA & LDA
Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Introduction to Machine Learning: Linear Models
Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Nearest Neighbor Search: Johnson-Lindenstrauss Lemma
Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.
Unsupervised Learning: Dimensionality Reduction
Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.
Principal Component Analysis: Applications and Limitations
Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.