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Probabilistic Machine Learning

Eberhard Karls University of Tübingen via YouTube

Syllabus

Welcome back — Summer 2021.
Probabilistic ML - Lecture 1 - Introduction.
Probabilistic ML - Lecture 2 - Reasoning under Uncertainty.
Probabilistic ML - Lecture 3 - Continuous Variables (updated 2021).
Probabilistic ML - Lecture 4 - Sampling.
Probabilistic ML - Lecture 5 - Markov Chain Monte Carlo.
Probabilistic ML - Lecture 6 - Gaussian Distributions.
Probabilistic ML - Lecture 7 - Gaussian Parametric Regression.
Probabilistic ML - Lecture 8 - Learning Representations.
Probabilistic ML - Lecture 9 - Gaussian Processes.
Probabilistic ML - Lecture 10 - Understanding Kernels.
Probabilistic ML - Lecture 11 - Example of GP Regression.
Probabilistic ML - Lecture 12 - Gauss-Markov Models.
Probabilistic ML - Lecture 13 - Gaussian Process Classification.
Probabilistic ML - Lecture 14 - Generalized Linear Models.
Probabilistic ML - Lecture 15 - Exponential Families.
Probabilistic ML - Lecture 16 - Graphical Models.
Probabilistic ML - Lecture 17 - Factor Graphs.
Probabilistic ML - Lecture 18 - The Sum-Product Algorithm.
Probabilistic ML — Lecture 19 — Extended Example: Topic Modelling.
Probabilistic ML — Lecture 20 — Latent Dirichlet Allocation.
Probabilistic ML — Lecture 21 — Expectation Maximization (EM).
Probabilistic ML — Lecture 22 — Variational Inference.
Probabilistic ML — Lecture 23 — Tuning Inference Algorithms.
Probabilistic ML — Lecture 24 — Customizing Probabilistic Models.
Probabilistic ML — Lecture 25 — Making Decisions.
Probabilistic ML — Lecture 26 — Revision.
Thanks and Goodbye - Probabilistic and Statistical Machine Learning.

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Tübingen Machine Learning

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