This repository is part of our MLT もくもく会 Math Reading Sessions and the MLT Mathematics for Machine Learning Discussions.
- Singular Value Decomposition by Jayson Cunanan, Ph.D., AI Researcher/Engineer at AI inside 株式会社
- Intro to Principal Component Analysis and Probabilistic PCA by Hiroshi Urata, Data Scientist, Data Scientist, IBM Japan
- Fourier transforms and a brief comparison with SVD by Jayson Cunanan, Ph.D., AI Researcher/Engineer at AI inside 株式会社
- ML Math Review Session: Singular Value Decomposition by Emil Vatai, Postdoctoral Researcher, RIKEN, Japan (Review Chapter 4: Matrix Decompositions)
- ML Math Review Session: Groups, residue classes by Emil Vatai, Postdoctoral Researcher, RIKEN, Japan (Review Chapter 2: Linear Algebra)
- ML Math Review Session: Gaussian Mixture Models by Pavitra Chakravarty, Data Analyst, Converging Health, Dallas, TX USA (Review Chapter 11: Density Estimation with Gaussian Mixture Models)
Our もくもく会 ML Math Reading Sessions were based on "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/
Sessions were held bi-weekly in different time zones on Sundays (PST, EST, GMT, CET, IST) and Mondays (APAC).
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines