Noisy Gradient Descent Algorithms

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PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Nonlinear Dimension Reduction Methods

Explores nonlinear dimension reduction methods preserving neighborhoods in lower-dimensional spaces.

Nearest Neighbor Classifiers and Curse of Dimensionality

Explores nearest neighbor classifiers, bias-variance tradeoff, curse of dimensionality, and generalization bounds in supervised machine learning.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Dimensionality Reduction

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Principal Component Analysis: Introduction

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Data Streams: Algorithms and Applications

Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Functional Linear Regression: Sparse Estimation and Adaptive Methods

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.