Rigorous Probability and Statistics - Part 2

We have coordinated with Michal Fabinger on a probability series which will be hosted by sister meetup Silicon Valley Hands On Programming Events (https://www.meetup.com/HandsOnProgrammingEvents/)

This is the second session of a 4-part series of lectures on Probability and Statistics starting from the beginning and proceeding in an intuitive, but mathematically rigorous way. (Similar Machine Learning lectures could also be scheduled.)

These lectures were very popular at the University of Tokyo and at meetups in Japan.

Topics for the second meeting: Sigma-algebras for events. Borel sigma-algebras for events corresponding to continuous sample spaces. Random variables. Examples of random variables.

Tokyo Data Science: https://tokyodatascience.com/courses
Twitter: https://twitter.com/fabinger
LinkedIn: https://www.linkedin.com/in/fabinger
Acalonia School: https://acalonia.com/
Meetup event in Tokyo: https://www.meetup.com/Machine-Learning-Tokyo/events/283334737/

The lectures should help Machine Learning practitioners and researchers to understand academic papers and to implement their methods. They should also help people pursuing academic paths in various scientific disciplines.

Separately, we plan to organize a group for those who want to engage in self-directed projects or work group projects. Among the different possible topics, there has been interest in causal inference, so this is certainly a possibility. (Please let us know if you are interested in this advanced group already now.)

Information about this series is in our talks repo: https://github.com/SanDiegoMachineLearning/talks
This is an example of the first lecture given several days ago:
https://www.youtube.com/watch?v=MxZN5F8iYLM