機械学習:Machine Learning

Symbolic Logic

Protected: Quasi-experimental design – how to derive causal relationships from observed data

How to verify causality for digital transformation, artificial intelligence , and machine learning tasks by first having the data for causal inference and then verifying causality from there.
2022.03.25
Symbolic Logicグラフ理論推論技術:inference Technology数理論理学機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
Symbolic Logic

Protected: Correlation, Causation and Relational Structure (2) Backdoor Criteria

Actual backdoor criteria for narrowing down variables to observe intervention effects in causal inference for digital transformation , artificial intelligence, and machine learning tasks
2022.03.23
Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
Symbolic Logic

Protected: Discrepancy between correlation (regression coefficient) and causation (intervention effect)

Differences between regression coefficients and intervention effect values for digital transformation, artificial intelligence , and machine learning tasks.
2022.03.22
Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習
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...
2022.03.21
IOT技術:IOT TechnologyStream Data Processingアルゴリズム:Algorithms最適化:Optimization機械学習:Machine Learning
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...
2022.03.21
Symbolic Logicアルゴリズム:Algorithmsグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
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...
2022.03.20
グラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
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...
2022.03.20
グラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
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
2022.03.18
Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
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)
2022.03.17
Symbolic Logicグラフ理論推論技術:inference Technology数理論理学最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics
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...
2022.03.16
グラフ理論ベイズ推定推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
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