Lectures related to Thomas Bayes

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

Bayes Theorem: Applications and Interpretation

Explores the practical utility of Bayes Theorem in inverting perspectives and efficiently calculating conditional probabilities.

Bayes' Theorem: Applications and Simulations

Covers the application of Bayes' Theorem in practical reasoning, especially in clinical settings.

Bayes Risk and Generalization in Machine Learning

Explores Bayes risk, generalization, error rates, and interpolation methods in machine learning.

Sampling: conditional maximum likelihood estimation

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Probability and Statistics: Discrete Random Variables

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Probability and Statistics

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Sampling: maximum likelihood estimation

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Ensemble Methods: Random Forests

Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.

Conditional Probability

Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.

Advanced Probability: Bayes' Theorem and Random Variables

Covers advanced probability concepts, including Bayes' Theorem and Random Variables.

Mixture models: individual level parameters

Explores mixture models and individual-level parameters in discrete choice scenarios, covering distribution, Bayes theorem, and expected values.

Elements of Statistics

Introduces key statistical concepts through practical exercises and MATLAB applications.

Bayes' Theorem: Defective Parts Detection

Explores Bayes' Theorem for defective parts detection, discrete random variables, and distribution functions, with practical examples and exercises.

Probability: Short History and Basic Concepts

Explores the short history of probability and basic concepts like sample space and event.

Mock Exam Session: Instructions and Solutions

Guides students through a mock exam session and provides insights into solving probability and discrete mathematics exercises.

Understanding Statistics & Experimental Design

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Bayesian Decision Theory: Utility, Risk, and Classification

Covers Bayesian decision theory, cost functions, classification, and decision rules.

Advanced Probability: Probability Trees and Conditional Probabilities

Explores probability trees, conditional probabilities, Bernoulli trials, binomial distribution, and Bayes' Theorem.