What we're about

Machine Learning Tokyo (MLT) is an award-winning nonprofit organization 一般社団法人 based in Japan, operating globally and remotely. MLT is dedicated to democratizing Machine Learning through open education, open source and open science. We support a research- and engineering community of 10,000 members.

Open Education –  MLT held more than 300 AI workshops, study sessions, talks and hackathons with thousands of participants in Tokyo and with remote participants from all over the world. Our events are inclusive and with an open education mindset, individuals can attend all events free of charge.
Open Source – Several volunteer teams within the MLT community are working on Machine Learning, Deep Learning, Reinforcement Learning and Robotics projects, including substantial work that has been done in the field of AI for Social Good. All projects are hosted on the public Machine Learning Tokyo GitHub Organization; code bases and repositories are published as open source projects.
Open Science – MLT teams have published research papers at international ML conference workshops and we’re continuously collaborating with Universities and Research Institutes in Japan to support open science and researchers with diverse academic backgrounds, including the University of Tokyo, Tokyo Institute of Technology and RIKEN CBS. We organized lectures, bootcamps and workshops on Machine Learning, Deep Learning and Data Science.

Find more information about MLT:
Website: https://www.mlt.ai/
Twitter: https://twitter.com/MLT
LinkedIn: https://www.linkedin.com/company/mltokyo/

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

● SUBSCRIBE ●
Subscribe to our monthly newsletter: https://www.getrevue.co/profile/mltai

● FIND MLT RESOURCES ●
Github: https://github.com/Machine-Learning-Tokyo
Youtube: https://www.youtube.com/MLTOKYO

Join us on Slack: https://bit.ly/2Yb0uXI

● 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

Upcoming events (1)

Statistics: Linear regression models 4

Needs a location

We're excited to continue the lectures on Statistics. They are a part of a 2022 lecture series that aims to build a solid foundation of statistics knowledge for the participants. These 2 lectures by Michal Fabinger cover a core aspect of statistics work: hypothesis testing. The relevant concepts are introduced in an intuitive yet rigorous way.

📌 To sign up for the whole lecture series, please fill out this form:
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.

(A) Normal linear model
📌 Topics discussed: Assumptions of the normal linear model, distribution
of the error term, a least-squares estimator of the parameter vector and
its distribution, estimator of the variance of the error term, and its
distribution, confidence intervals, and hypothesis tests for the model's parameters, Student's t-distribution, and chi-squared distribution.

(B) Linear model with heteroscedasticity
📌 Topics discussed: Assumptions of the linear model with heteroscedasticity, distribution of the error term, least-squares the estimator of the parameter vector and its asymptotic distribution, confidence intervals and hypothesis tests for the model's parameters.

👉 JOIN ZOOM
https://zoom.us/j/95671732143?pwd=Q2w0TjFIeC9LcVhWMndnbjc1NWNLQT09

👉 Lecturer: Michal Fabinger, https://twitter.com/fabinger

👉 Bio: Michal is the founder of the Acalonia school (acalonia.com,
formerly 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) makes this possible even for those who currently lack finances. Michal's research is in physics and economics, with the corresponding Ph.D. 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

1

Past Events

Statistics: Linear regression models 3

Needs a location

Photos (233)