Symbolic Logic Mathematics in Machine Learning
On the mathematics underlying all computer programming and algorithms, including digital transformation (DX), artificial intelligence (AI), and machine learning (ML)
数学:Mathematics
Symbolic Logic 数学:Mathematics Decision Theory and Mathematical Decision Making Techniques
We will discuss mathematical decision-making techniques used in reinforcement learning, online prediction, and algorithms for high-speed automated stock trading. After describing the four decision strategies, I will introduce subjective probability, Bayesian theory, multiple concept theory, and supply rise theory.
機械学習:Machine Learning Protected: Matrix Decomposition -Extraction of relational features between two objects
Extraction of relationships by machine learning, matrix factorization approach, non-negative matrix factorization
機械学習:Machine Learning Protected: Clustering Techniques for Asymmetric Relational Data – Probabilistic Block Model and Infinite Relational Model
Machine Learning Extraction of Relationships, Probabilistic Block Model and Infinite Relation Model
最適化:Optimization Protected: Clustering of symmetric relational data – Spectral clustering
Extraction of relationships, knowledge extraction and prediction, spectral clustering by machine learning for graph analysis, etc.
アルゴリズム:Algorithms Protected: Variational Bayesian Learning Introduction
Fundamentals of Variational Methods for Optimizing Bayesian Estimation in Machine Learning
アルゴリズム:Algorithms Protected: Online Machine Learning Overview
Basics of online learning for sequential learning from a small number of supervised data
アルゴリズム:Algorithms Protected: Bayesian Deep Learning – Introduction
Overview of Bayesian deep models, an evolution of deep learning and probabilistic generative models.
推論技術:inference Technology Protected: Graphical Model Overview and Bayesian Network
Graphical model overview for efficient approach to stochastic generative models, Bayesian networks
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