DX | Page 22 | Deus Ex Machina

Protected: Application of Variational Bayesian Algorithm to Matrix Decomposition Models

Variational Bayesian learning and empirical variational Bayesian learning algorithms for matrix factorization models as computational methods for stochastic generative models utilized in digital transformation , artificial intelligence , and machine learning tasks

2022.08.18

アルゴリズム:Algorithms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線形代数:Linear Algebra

Protected: Computation of graphical models without hidden variables

Maximum likelihood, Bayesian, and variational computations of graphical models without hidden variables in probabilistic generative models utilized in digital truss formation, artificial intelligence, and machine learning tasks, learning by the pseudolikelihood function, Bethe approximation, parameter estimation by TRW upper bound, variational methods, entropy functions, IPF algorithm, MAP estimators

2022.08.17

アルゴリズム:Algorithms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線形代数:Linear Algebra

Protected: Non-patometric Bayes and clustering (2) Stochastic model of partitioning and Dirichlet processes

Clustering using nonparametric Bayes, one of the applications of probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks (Chinese restaurant process and Dirichlet process and concentration parameter estimation, bar-folding process)

2022.08.16

アルゴリズム:Algorithms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線形代数:Linear Algebra

Stochastic Generative Models and Gaussian Processes(1) Basis of Stochastic Models

Stochastic generative models for digital transformation, artificial intelligence, and machine learning tasks and fundamentals of stochastic models to understand Gaussian processes (independence, conditional independence, simultaneous probability, peripheralization and graphical models)

2022.08.15

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization確率・統計:Probability and Statistics線形代数:Linear Algebra

stochastic optimization

Stochastic optimization methods for solving large-scale learning problems on large amounts of data used in digital transformation, artificial intelligence, and machine learning tasks supervised learning and regularization, basics of convex analysis, what is stochastic optimization, online stochastic optimization, batch stochastic optimization, stochastic optimization in distributed environments

2022.08.14

アルゴリズム:Algorithmsオンライン学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Extraction of tabular data from the Web and documents and semantic annotation (SemTab) learning

Extraction of tabular data from the Web and documents and semantic annotation learning, one of the data extraction tasks utilized in digital transformation, artificial intelligence, and machine learning tasks, with a focus on the ISWC workshop SemTab

2022.08.13

web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地理空間情報処理時系列データ解析

Statistical Learning Theory

Theory on statistical properties of machine learning algorithms utilized in digital transformation, artificial intelligence, and machine learning tasks (law of large uniform numbers, universal kernel, discriminant fitting loss)

2022.08.13

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

About WoT (Web of Things) technology

Overview and challenges of the Web of Things WoT, an extension of IoT technology used for digital transformation, artificial intelligence, and machine learning tasks

2022.08.12

IOT技術:IOT TechnologyStream Data Processingweb技術:web technologyセマンテックウェブ技術:Semantic web Technology

Protected: Gaussian Processes – The Advantages of Functional Clouds and Their Relationship to Regression Models, Kernel Methods, and Physical Models

Gaussian Processes as Applications of Stochastic Generative Models for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks Miscellaneous Function Clouds Advantages and Regression Models and their Relationship to Kernel Methods and Physical Models

2022.08.11

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Theory and Algorithms for the Bandit Problem

The theory and algorithms of the Bandit Problem for selecting optimal strategies to be utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.08.11

バンディッド問題強化学習