ML Math Reading Session #4 (APAC)

This is Session #4 of 11 fully remote Machine Learning Math Reading Sessions. In addition to the remote session members can join us this time at the Spaces Shinagawa co-working space for a Meetup.

● 6:30 pm – Doors open
● 7:00 pm – Kick-off

We'll go through “Mathematics For Machine Learning” by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, to be published by Cambridge University Press. https://mml-book.github.io/

Join Zoom Meeting
https://zoom.us/j/241984895

You can find more information about the book, the sessions and how to join the communication channels here https://machinelearningtokyo.com/2019/11/28/ml-math-reading-sessions/

The goal is to be more disciplined and create a new collaborative and interactive way of studying. More than 1000 people from all over the world expressed their interest to be part of this, and luckily, we have found a great international leadership team that will lead the sessions in different time zones from January.

📌 Table of Contents

● Part I: Mathematical Foundations

• Introduction and Motivation
• Linear Algebra (Session #1)
• Analytic Geometry (Session #2)
• Matrix Decompositions (Session #3)
• Vector Calculus (Session #4)
• Probability and Distribution (Session #5)
• Continuous Optimization (Session #6)

● Part II: Central Machine Learning Problems

• When Models Meet Data
• Linear Regression
• Dimensionality Reduction with Principal Component Analysis
• Density Estimation with Gaussian Mixture Models
• Classification with Support Vector Machines

● THANK YOU
Huge THANK YOU goes out to Spaces Shinagawa for hosting the Meetup at their co-working space. https://www.spacesworks.com/tokyo/shinagawa/

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