R language and Machine Learning
Overview of the R language as a general-purpose tool for machine learning
機械学習
Protected: Theory of noiseless L1-norm minimization(The problem of finding sparse solutions that satisfy linear equations)
Sparse model machine learning and norms for data compression and feature extraction
Protected: Machine Learning Based on Sparsity (3) Introduction of Sparsity and L1 Norm
Sparse model machine learning and norms for data compression and feature extraction
Stream Reasoning Architecture (Overview)
Machine learning and system architecture for data streams (time series data) used for digital transformation (DX), artificial intelligence (AI), machine learning (ML), and cross-fusion use of IOT, Stream Resoning system, and ontology
Reasoning Technology
Overview of inference techniques used in digital transformation (DX), artificial intelligence (AI), machine learning (ML), etc.
Protected: Explainable Machine Learning (6) Interpretable Model (RuleFit)
Overview of interpretable models using RuleFit, explainable machine learning used in digital transformation (DX), artificial intelligence (AI), and machine learning (ML) (about RuleFit, a model generated by Lasso and ensemble learning)
Mathematics in Machine Learning
On the mathematics underlying all computer programming and algorithms, including digital transformation (DX), artificial intelligence (AI), and machine learning (ML)
Protected: Explainable Machine Learning (5) Interpretable Models (Decision Rules)
Explainable Machine Learning that can be used in Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML). Overview of interpretable models with decision rules (OneR, Sequential Covering, Bayesian Rule List).
Protected: Explainable machine learning (3) Interpretable models (GLM, GAM)
Digital Transformation (DX), Artificial Intelligence (AI), Explainable Machine Learning for Machine Learning (ML) Applications, Generalized Linear Models, GLM, Generalized Additive Models, GAM
Protected: Explainable artificial intelligence (2) Interpretable models (logistic regression model)
Explain machine learning and logistic regression for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) applications.