推論技術:inference Technology Protected: Analysis of time series data (2) Basics of statistical analysis
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数学:Mathematics
推論技術:inference Technology 機械学習:Machine Learning Protected: Analysis of Time Series Data (1) Introduction
Overview of time series data analysis methods based on probability models used in digital trasformation (DX), artificial intelligence (AI), and machine learning (ML).
ベイズ推定 Probabilistic approaches in machine learning
Probabilistic Generative Models Used in Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML)
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 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