machine learning

Protected: Fundamentals of Submodular Optimization (3)Algorithm for Submodular Function Minimization Problem Using the Minimum Norm Point of the Fundamental Polyhedron

Algorithm for a submodular function minimization problem using base polyhedral minimum norm points, one of the methods of optimization methods (submodular optimization) for discrete information used in digital transformation, artificial intelligence, and machine learning tasks.

2022.04.15

Symbolic Logicアルゴリズム:Algorithmsグラフ理論微分積分:Calculus推論技術:inference Technology数理論理学最適化:Optimization確率・統計:Probability and Statistics関係データ学習

Protected: Fundamentals of Submodular Optimization (2) Basic Properties of Submodular Functions

Three basic properties of submodular functions (normalized, non-negative, symmetric) as a basis for optimization algorithms (submodular optimization) of discrete information for digital transformation, artificial intelligence and machine learning tasks and their application to graph cut maximization and minimization problems

2022.04.14

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: Fundamentals of Submodular Optimization (1) Definition and Examples of Submodular Functions

Submodular functions (cover functions, graph cut functions, concave functions) and optimization as a basis for discrete information optimization algorithms for digital transformation, artificial intelligence, and machine learning tasks

2022.04.13

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関係データ学習

Protected: Introduction to Amazon Web Services Networking(1) – On-Premise Systems and AWS Overview

Fundamentals for building AWS cloud services for digital transformation, artificial intelligence, and machine learning task utilization (differences from on-premise systems and AWS system configuration overview)

2022.04.12

web技術:web technologyデータベース技術:DataBase Technology非同期/並行処理:Asynchronous/parallel processing

Web page development using Laravel (1) Overview of Laravel and environment settings

Overview of Laravel, a low learning cost Docker-based web page creation framework used for digital transformation, artificial intelligence, and machine learning tasks, and how to set up your environment

2022.04.11

PHPweb技術:web technologyデータベース技術:DataBase 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

Protected: Effectiveness of “nursery development” verified by difference in difference

Actual causal inference using the difference-in-differences method, one of the causal inference methods relationship between daycare center development and female employment rate

2022.04.08

Symbolic Logicグラフ理論推論技術:inference Technology数理論理学:Mathematical logic時系列データ解析最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics関係データ学習

Protected: Estimating Bunt Effects Using Propensity Scores

Estimating baseball bunt effects using propensity scores as an application of causal inference for digital transformation, artificial intelligence, and machine learning tasks.

2022.04.07

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: Application of causal effect estimation – Causal and adjustment effects of commercial contact

Specific applications of statistical causal inference used in digital transformation, artificial intelligence, and machine learning tasks (causal and adjusted effects of CM contact using average treatment effect ATE and average treatment effect ATT in treatment groups)

2022.04.06

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: Basics of Statistical Causal Effects (2)Methods Using Regression Models and Matching and Stratified Analysis

Regression modeling methods for statistical causal inference utilized in digital transformation, artificial intelligence, and machine learning tasks; causal effect estimation using matching and stratified analysis methods

2022.03.30

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: Basics of Statistical Causal Effects (1)Definition of Causal Effects Based on the Rubin Effect Model

Definition of causal effects and estimation of statistical causal effects (ATT, ATU, ATE) based on the Rubin effect model used for digital transformation, artificial intelligence and machine learning tasks

2022.03.29

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