K-means and Gaussian Mixture Model

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Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Probabilistic Interpretation: K-Means & GMM

Explores the probabilistic interpretation of K-means clustering and its relation to Gaussian Mixture Models.

Interpretation of Entropy

Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.

Eigenvalues and EM Algorithm

Covers eigenvectors, principal components, likelihood variables, EM algorithm, Jensen's inequality, and maximizing lower bounds.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Dimensionality Reduction: PCA and LDA

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

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Introduction to Convexity

Introduces the key concepts of convexity and its applications in different fields.