IOT技術:IOT Technology Submodular Optimization and Machine Learning
Overview of Machine Learning with Submodular Optimization
Submodular functions are a concept corresponding to c...
機械学習:Machine Learning
IOT技術:IOT Technology Symbolic Logic Machine Learning Professional Series “Submodular Optimization and Machine Learning” reading notes
Machine Learning Professional Series "Submodular Optimization and Machine Learning" reading notes
Submodular f...
グラフ理論 Bayesian inference and MCMC open source software
Bayesian inference and MCMC open source software
Bayesian statistics means that not only the data, but also th...
グラフ理論 Machine Learning Startup Series – Introduction to Machine Learning with Bayesian Inference Reading Notes
Summary
Bayesian estimation can be one of the statistical methods for interpreting data and learning models fro...
Symbolic Logic Protected: Causal InferenceIntroduction(2)Stratified Analysis and Regression Modeling
Theory and practice of causal inference through analysis by stratified analysis and regression models for statistical causal estimation used in digital transformation , artificial intelligence , and machine learning tasks
Symbolic Logic Protected: Introduction to Causal Inference (1) Confounding Factors and Randomized Experiments
Introduction to statistical causal inference (randomized experiments controlling for confounding factors to distinguish between causality and pseudo-correlation)
グラフ理論 The World of Bayesian Modeling
Overview
This presentation provides an overview of the contemporary world of Bayesian modeling from the perspec...
グラフ理論 Machine Learning Professional Series “Graphical Models” reading notes
Summary
Bayesian estimation can be one of the statistical methods for interpreting data and learning models from...
グラフ理論 Nonparametric Bayesian and Gaussian Processes
Overview
Nonparametric Bayes is a method of Bayesian statistics, an "old and new technique" that was already theo...
Symbolic Logic Protected: Statistical Causal Search – Extended Approach
Extension of LiNGAM approach assumptions (linearity, acyclicity, non-Gaussianity) in statistical causal inference used in digital transformation , artificial intelligence , and machine learning tasks