バンディッド問題

Protected: Fundamentals of Stochastic Bandid Problems

Basics of stochastic bandid problems utilized in digital transformation, artificial intelligence, and machine learning tasks (large deviation principle and examples in Bernoulli distribution, Chernoff-Heffding inequality, Sanov's theorem, Heffding inequality, Kullback-Leibler divergence, probability mass function, hem probability, probability approximation by central limit theorem).
微分積分:Calculus

Protected: Fundamentals of Convex Analysis as a Basic Matter for Sequential Optimization in Machine Learning

Basics of convex analysis as a fundamental matter of continuous optimization utilized in digital transformation, artificial intelligence, and machine learning tasks subgradient, subdifferential, conjugate function, closed truly convex function, conjugate function, strongly convex function, closed truly convex function, upper and lower bounds on function values, Hesse matrix, epigraph, Taylor's theorem, relative interior, Ahuynh envelope, continuity, convex envelope, convex function, convex set
アルゴリズム:Algorithms

Protected: Basic Framework of Statistical Mathematics Theory

Basic framework of statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks regularization, approximation and estimation errors, Höfding's inequality, prediction discriminant error, statistical consistency, learning algorithms, performance evaluation, ROC curves, AUC, Bayes rules, Bayes error, prediction loss, empirical loss
アルゴリズム:Algorithms

Protected: Supervised learning and regularization

Overview of supervised learning regression, discriminant and regularization ridge function, L1 regularization, bridge regularization, elastic net regularization, SCAD, group regularization, generalized concatenated regularization, trace norm regularization as the basis of machine learning optimization methods used for digital transformation, artificial intelligence and machine learning tasks
アルゴリズム:Algorithms

Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization

Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch

Christianity in Japan and the Roads of Shimabara and Amakusa

Ryotaro Shiba's Roads in Shimabara and Amakusa and Christianity in Japan Oe Tenshudo, Father Garnier, Hara Castle, Amakusa Shiro, Kunimitsuya, Shimabara Teppocho, samurai residences, Mount Unzen, Unzen Hot Springs, Okita Battlefield Site, Shimabara Castle, Hidden Christians, Francis Xavier, Jesuits, Isahaya, Nagasaki Airport
キリスト教

Reading the Core of Christianity What is the New Testament?

Reading the Core of Christianity What is the New Testament (Gospel of Luke, On the Way to Emmaus, Jesus Walking with Us, Resurrection, Last Supper, Deuterocanonical, Gospel of John, I Am, Parable of the Prodigal Son, Parable of the Good Samaritan)
Clojure

UML, workflow data visualization tool plantUML

UML for digital transformation , artificial intelligence , and machine learning tasks, workflow data visualization tools plantUML
アルゴリズム:Algorithms

Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method

Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit
Clojure

Hierarchical Temporal Memory and Clojure

Deep learning with hierarchical temporal memory and sparse distributed representation with Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
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