Relevance Vector Machine: Addressing SVM Shortcomings

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Page 2 of 4

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

Supervised Learning Fundamentals

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

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Inference Problems & Spin Glass Game

Covers inference problems related to the Spin Glass Game and the challenges of making mistakes with preb P.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.

Estimation & Bayesian Inference

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Bayesian Extremes: Markov Chain Monte Carlo

Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.