推論技術:inference Technology Protected: Graphical Models Overview and Markov Probability Fields
Graphical model overview for efficient approach to stochastic generative models, Markov stochastic processes
確率・統計:Probability and Statistics
推論技術:inference Technology 推論技術:inference Technology Protected: An overview of the expert integration problem in online forecasting and its implementation in Regret
Overview of online predictive learning for solving sequential prediction problems, introduction to Regret
最適化:Optimization Protected: Nonparametric Bayesian Point Processes and the Mathematics of Statistical Machine Learning Overview
An overview of the nonparametric Bayesian method, a probability generation model in infinite dimensions
アルゴリズム:Algorithms Protected: Introduction to Machine Learning with Bayesian Inference
An overview of machine learning with Bayesian inference, a probabilistic generative model.
微分積分:Calculus Protected: Submodular Optimization and Machine Learning – Overview
Overview of inferior modular optimization, which is machine learning for discrete variables used in sensor placement optimization.
最適化:Optimization Protected: Causal Exploration of Statistics – Introduction
Overview of methods for statistical learning of causal information, and methods for determining various causal relationships.
最適化:Optimization Protected: Gaussian Processes and Machine Learning – Introduction
Overview of Gaussian Generative Models for Machine Learning without Parameterization of Probabilistic Generative Models
アルゴリズム:Algorithms Protected: Mixed Unigram Model
Basics of topic models for classification of document data, compound unigram models and LDA for teaching beginners
アルゴリズム:Algorithms Protected: Unigram Model
Basics of topic model for classification of document data for education of beginners, unigram model
機械学習:Machine Learning Protected: Bayesian Estimation and Information Theory
Bayesian estimation and information theory for artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), from de Moivre's probability theory to Bayesian probability and Shannon's information engineering and tools for Bayesian estimation (STAN)