Name: Statistics: Maximum likelihood estimation 3
Start: 2022-09-20T19:45:00+09:00
End: 2022-09-20T20:45:00+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.

● MLT NEWSLETTER
Sign up for the (infrequent and low noise) newsletter if you'd like to stay up to date with events and what we're working on https://www.getrevue.co/profile/mltai

● MLT PATRON
Become an MLT Patron and help us to keep MLT meetups like this inclusive and for free. https://www.patreon.com/MLTOKYO

● FIND MLT RESOURCES
Github: https://github.com/Machine-Learning-Tokyo
Youtube: https://www.youtube.com/MLTOKYO
Slack: https://bit.ly/36ImxtW

● RECRUITING
MLT events are for community building and knowledge sharing. We politely ask that company representatives, recruiters, and consultants looking to hire or sell their services do not participate in MLT activities or approach members in any form.

● CODE OF CONDUCT
MLT promotes an inclusive environment that values integrity, openness, and respect. https://github.com/Machine-Learning-Tokyo/MLT_starterkit

Suzana

Michal Fabinger

Machine Learning Tokyo

Artificial_Intelligence

Technology

Neural Networks

Big Data

Artificial Intelligence Applications

Predictive Analytics

New Technology

Machine Learning

Natural Language Processing

Machine Intelligence

Data Science

Deep Learning

Artificial Intelligence

Open Source

Statistics: Maximum likelihood estimation 3

Online event

Share

Machine Learning Tokyo

Statistics: Maximum likelihood estimation 3

Machine Learning Tokyo

Details

Members are also interested in