Probability Primer for Probabilistic Robotics (Cyrill Stachniss) 

00:00 - Background & intro 01:37 - Recurring Terms in this Course 04:05 - State Estimation Example: Localization and Mapping 04:53 - Probabilistic Approaches 07:08 - (beginning of) Probability Primer 07:23 - Why Probabilities? 08:13 - Axioms of Probability Theory 14:17 - Discrete Random Variables 16:11 - Continuous Random Variables 18:33 - Joint and Conditional Probability 21:41 - Law of Total Probability 23:03 - Marginalization 24:04 - Example 1 (on Conditional & Marginalized Probability) 26:52 - Example 2 (on Conditional & Marginalized Probability) 32:10 - Bayes' Rule 34:34 - Bayes' Rule with Background Knowledge 35:04 - Conditional Independence 37:58 - Normal Distribution 39:10 - Multivariate Normal Distribution 40:00 - Gaussian Mixture 41:26 - Summary

Thank you professor. Very thoughtful of you to prepare a material complete for those interested in studying this field.

Best review of the essentials of probability theory I've seen. Thank you so much!

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Thanks a lot Professor for your knowledge sharing.

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Amazing channel, thanks for all the content. I am following a master's degree in Computer Vision and photogrammetry seems to be very interesting.

It was very helpful. thank you very much prof

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can you suggest sources to revise Multivariate Normal Distribution and Gaussian Mixture?