Name: Statistics: Maximum likelihood estimation 4
Start: 2022-10-04T19:45:00+09:00
End: 2022-10-04T20:45:00+09:00

These 4 lectures by Michal Fabinger cover a frequently used statistical method: maximum likelihood estimation. The concepts are introduced in an intuitive yet rigorous way.

The topics include Likelihood and conditional likelihood and their maximization, maximum likelihood estimation of linear models, logit
models, models/logistic regression, probit models, and Poisson
regression, score functions, Fisher information and asymptotic
confidence intervals for model parameter estimates.

The lectures are a part of a 2022 lecture series that aims to build a
solid foundation of statistics knowledge for the participants. To sign
up for the whole lecture series, please fill out this form: [https://form.typeform.com/to/rep1RuEc](https://form.typeform.com/to/rep1RuEc)

The material should later help the participants understand scientific
articles that use probability theory and statistics. Such knowledge is
useful both for machine learning and data science practitioners and
for those on an academic path (undergraduates, graduate students,
postdocs, or faculty members). The content is similar to the
corresponding course at the Acalonia school.

📌 Lecturer: Michal Fabinger [https://twitter.com/fabinger](https://twitter.com/fabinger)

📌 Bio: Michal is the founder of the Acalonia school ([acalonia.com](http://acalonia.com/),
formerly [tokyodatascience.com](http://tokyodatascience.com/)), which aims to build an education
system for a world where location does not matter. The school provides
a straightforward way for talented people from developed and
developing countries to improve their skills for their current jobs,
get new knowledge-demanding jobs, or get admitted to graduate schools.
The Fair Play Tuition system ([acalonia.com/fair-play](http://acalonia.com/fair-play)) makes this
possible even for those who currently lack finances. Michal's research
is in physics and economics, with the corresponding PhD training
completed at Stanford and Harvard. At the University of Tokyo and the
Pennsylvania State University, Michal taught courses on Deep Learning,
Data Science, Statistics, Asset Pricing, International Trade,
International Finance, and Development Economics.

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Suzana

Michal Fabinger

Machine Learning Tokyo

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Statistics: Maximum likelihood estimation 4

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Statistics: Maximum likelihood estimation 4

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