Decision Making Under Uncertainty

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Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Dimensionality Reduction: PCA and LDA

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

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Nearest Neighbor Rules: Part 2

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

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Scientific Machine Learning: Introduction to Spin Glass Models

Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.

Machine Learning: Types and Applications

Covers the types of machine learning, including supervised, unsupervised, and reinforcement learning.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Machine Learning: Basics and Applications

Covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.