Probabilistic Machine Learning
Tübingen Machine Learning via YouTube
- 0
- 40 hours worth of material
- YouTube
- English
Course Overview
This playlist collects the lectures on Probabilistic Machine Learning by Philipp Hennig at the University of Tübingen during the Summer Term of 2020.
The lectures were recorded for online teaching during the Covid19 pandemic. They are publicly available under the Creative Commons license. More information on the course, and course material (with embedded, time-stamped links to the youtube videos) can be found at https://uni-tuebingen.de/en/134452.
The course covers the probabilistic ("Bayesian") paradigm for machine learning, and occasionally draws direct connections to statistical (e.g. Lecture 10) and deep learning (e.g. Lecture 8). The course is aimed at master students in computer science and related fields.
Course Circullum
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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This Course Include:
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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- Language:English
- Duration:40 hours worth of material
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