Rigorous Probability and Statistics

Mar 1, 2022 · Mountain View, United States of America

This event is for the 4th lecture.

This is a series of 4 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.)

There are resources associated with the lectures: notes, quizzes, and homework tasks. The following is a link to sign up for the resources.


These lectures were very popular at the University of Tokyo and at meetups in Japan. The topics of the four lectures are the following:

Lecture 1: 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.

Lecture 2: Sigma-algebras for events. Borel sigma-algebras for events
corresponding to continuous sample spaces. Random variables. Examples of random variables.

Lecture 3: Distributions of random variables. Cumulative distribution functions,
probability mass functions, and probability density functions.
Examples of distributions.

Lecture 4: Transformations of random variables. Transformations of cumulative distribution functions, probability mass functions, and probability density functions. Examples of usage of transformed 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 understand academic papers and to implement their methods. They should also help people pursuing academic paths in various scientific disciplines.

This is an example of the first lecture given several days ago:

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. Please send an email to [masked] if you are interested.

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