Principal Component Analysis: Dimensionality Reduction

Related lectures (30)

Page 3 of 3

Introduces the Young-Eckart-Mirsky theorem and PCA for unsupervised learning and data visualization.

Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Covers key concepts and examples of machine learning algorithms and techniques.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Covers the challenges of high-dimensional data, the need for dimensionality reduction, and the implications for machine learning models.

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.