Bayes Theorem, Bayesian networks, Bayesian sampling methods, Bayesian inference, machine learning and much more

Bayes Theorem

Conditional & Absolute independence

Bayesian networks & d separation

Enumeration & Elimination

Sampling methods (rejection sampling, Gibbs sampling, Metropolis Hastings)

Bayesian inference

Continuous Bayesian statistics

Bayesian statistics & machine learning

High school level mathematics / ideally first-year university mathematics or statistics course

Basic background in probability

Bayesian Statistics is a fascinating field and today the centerpiece of many statistical applications in data science and machine learning. In this course, we will cover the main concepts of Bayesian Statistics including among others Bayes Theorem, Bayesian networks, Enumeration & Elimination for inference in such networks, sampling methods such as Gibbs sampling and the Metropolis-Hastings algorithm, Bayesian inference and the relation to machine learning.

This course is designed around examples and exercises that provide plenty of opportunities to build intuition and apply your gathered knowledge. Many examples come from real-world applications in science, business or eeering or are taken from data science job interviews.

While this is not a programming course, I have included multiple references to programming resources relevant to Bayesian statistics. The course is specifically designed for students without many years of formal mathematical education. The only prerequisite is high-school level mathematics, ideally a first-year university mathematics course and a basic understanding of probability.

University students in science, business and eeering interested in learning about Bayesian Statistics for university or job interviews

Practitioners in these fields interested in learning the central concepts of Bayesian statistics to apply them to real-world problems



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