Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK

About Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK

Probabilistic machine learning has gained a lot of practical relevance over the past 15 years as it is highly data-efficient, allows practitioners to easily incorporate domain expertise and, due to the recent advances in efficient approximate inference, is highly scalable. Moreover, it has close relations to causal inference which is one of the key methods for measuring cause-effect relationships of machine learning models and explainable artificial intelligence. This course will introduce all recent developments in probabilistic modeling and inference. It will cover both the theoretical as well as practical and computational aspects of probabilistic machine learning. In the course, we will implement all the inference techniques and apply them to real-world problems.

Publisher

Dr. Rainer Schlosser

Category

education

Language

de

Explicit

No

First episode

7 Apr 2025

Latest episode

7 Jul 2025

Latest episodes

14 episodes in the feed.

1. 

   7 Jul 2025

   Real-World Applications

   This episode of "Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK" explores real-world applications of probabilistic machine learning.

2. 

   30 Jun 2025

   Information Theory

   This episode of "Introduction to Probabilistic Machine Learning" likely explores information theory concepts, but lacks a specific description to detail the core insights.

3. 

   23 Jun 2025

   Gerichtete Graphen

   This episode of "Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK" likely explores directed graphs within the context of probabilistic machine learning.

4. 

   23 Jun 2025

   Non-Bayesian Classification

   This episode explores non-Bayesian classification methods within the context of probabilistic machine learning.

5. 

   16 Jun 2025

   Gaussian Processes

   This episode likely explores Gaussian Processes within the context of probabilistic machine learning, offering insights into this specific technique.

6. 

   11 Jun 2025

   Bayesian Classification

   This episode likely explores Bayesian classification within the context of probabilistic machine learning, offering insights into its principles.

7. 

   2 Jun 2025

   Bayesian Regression

   This episode likely explores Bayesian regression within the context of probabilistic machine learning, offering insights into statistical modeling techniques.

8. 

   26 May 2025

   Linear Basis Function Models

   This episode likely explores linear basis function models within the context of probabilistic machine learning, offering insights into their application.

9. 

   19 May 2025

   Bayesian Ranking

10. 

    12 May 2025

    Graphical Models: Approximate Inference

11. 

    5 May 2025

    Graphical Models: Exact Inference

12. 

    28 Apr 2025

    Graphical Models: Independence

13. 

    14 Apr 2025

    Inference & Decision Making

14. 

    7 Apr 2025

    History & Probability