Clustering: K-means & LDA

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Spectral Clustering: Theory and Applications

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Nearest Neighbor Rules: Part 2

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

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Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.