Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Real-World Applications
Jul 7, 20251h 22mS0
education
Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Hosted by Dr. Rainer Schlosser · education · DE
13 episodesAudience: 2
About this Podcast:
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.
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What is Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK about?
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.
Who hosts Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK?
Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK is hosted by Dr. Rainer Schlosser. The show has published 13 episodes to date.
What has Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK covered recently?
Recent episodes include "Real-World Applications" (7 July 2025, 82 minutes), "Information Theory" (30 June 2025, 86 minutes), and "Gerichtete Graphen" (23 June 2025, 58 minutes).
How often does Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK publish?
Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK publishes roughly weekly, with episodes averaging 86 minutes across 13 episodes.
How hard is it to get booked on Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK?
Booking probability for Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK is 27%, and the host is rated Selective on guests. Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK requires a Pod Score of 80 or higher. Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK's contact route on record: verified email.
Who should pitch Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK?
The strongest fits for Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK are Universities & Colleges. Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK has an education audience.
About the host
Dr. Rainer Schlosser hosts Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK, an education podcast with 13 episodes published.
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Recent episodes
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Information Theory
Jun 30, 20251h 26mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Gerichtete Graphen
Jun 23, 202558 minS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Non-Bayesian Classification
Jun 23, 20251h 30mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Gaussian Processes
Jun 16, 20251h 32mS0
View More
Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Bayesian Classification
Jun 11, 20251h 26mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Bayesian Regression
Jun 2, 20251h 26mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Linear Basis Function Models
May 26, 20251h 29mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Bayesian Ranking
May 19, 20251h 32mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Graphical Models: Approximate Inference
May 12, 20251h 29mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Graphical Models: Exact Inference
May 5, 20251h 30mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Graphical Models: Independence
Apr 28, 20251h 24mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
Inference & Decision Making
Apr 14, 20251h 31mS0Introduction to Probabilistic Machine Learning (ST 2025) - tele-TASK
History & Probability
Apr 7, 20251h 25mS0
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