Expectation Maximization and Clustering

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Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Gibbs Sampling: Simulated Annealing

Covers the concept of Gibbs sampling and its application in simulated annealing.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Clustering & Density Estimation

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

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Unsupervised Learning: PCA & K-means

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

Clustering & Density Estimation

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

Topic Models

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.