Rigorous Probability and Statistics

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 first session of a 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 first meeting: Types of probability distributions and the need for a rigorous mathematical framework. Probability spaces, sample spaces, event spaces, and probability measures. Examples of probability spaces.

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.)

This is an example of the first lecture given several days ago:
https://www.youtube.com/watch?v=MxZN5F8iYLM