digital transformation

Protected: React Practice Starting with Modern Javascript Basics – What is Modern Javascript ?

Modern Javascript fundamentals for React practices used for digital transformation , artificial intelligence and machine learning tasks virtual DOM, package manager, ES2015 and later, modular handlers, transpilers, SPA

2022.03.24

javascriptweb技術:web technologyセマンテックウェブ技術:Semantic web Technologyユーザーインターフェース/データビジュアライゼーション非同期/並行処理:Asynchronous/parallel processing

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

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

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

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

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

2022.03.15

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM with unobserved common cause (2) Approach to model unobserved common cause as a sum

LiNGAM approach to modeling unobserved common causes as sums to statistical causal inference for digital transformation, artificial intelligence , and machine learning tasks

2022.03.14

グラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM in the presence of unobserved common terms (1) Approach to explicitly incorporate unobserved common causes in the model by independent component analysis

LiNGAM approach to incorporate unobserved common causes into models with independent component analysis in statistical causal inference for digital transformation , artificial intelligence , and machine learning tasks.

2022.03.11

グラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM (4)Estimation of LiNGAM model (2)An approach using regression analysis and evaluation of independence

Application of LiNGAM estimation with an iterative regression distribution and independence assessment approach to statistical causal inference for use in digital transformation , artificial intelligence, and machine learning

2022.03.10

Symbolic Logicグラフ理論推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: LiNGAM (3)Estimation of LiNGAM model (1)Approach using independent component analysis and regression analysis

Estimation of LiNGAM models using independent component analysis (Hungarian method) and regression analysis (adaptive Lasso) for probabilistic causal search for digital transformation and artificial intelligence task applications

2022.03.09

Symbolic Logicグラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics