Protected: From Averages to Individuality: The Open World of Statistical Modeling (1)Statistical Model Overview
From statistics of averages to statistics of personality, using Bayesian modeling, Hierarchical Bayes
Protected: Introduction to Hierarchical Bayes (from GLM to Hierarchical Bayesian Model)
Bayesian inference that can be used for artificial intelligence (AI), natural language, and digital transformation (DX); generate hierarchical Bayesian models from GLM models to solve complex statistical models
Geospatial Information Processing
Free software for geospatial information that can be used for digital transformation (DX) and artificial intelligence (AI) tasks (QGIS), R) and information on the development of business and other applications.
Speech Recognition Technology
Application of deep learning and other machine learning techniques to speech signal processing for digital transformation and artificial intelligence tasks
Protected: Convolutional Neural Networks (2) CNN Overview and Implementation
Overview of convolutional neural networks, a deep learning technique used for image recognition.
Protected: Convolutional Neural Networks (1) Forward and Reverse Propagation Algorithms and Mini-Batch
Forward and back propagation algorithms and mini-batches as the basis for algorithms in DNNs that can be used for digital transformation and artificial intelligence tasks.
Deep Learning Technologies
Deep learning technology, one of the breakthroughs in artificial intelligence technology and machine learning technology
Overview of Kernel Methods and Support Vector Machines
On kernel methods, one of the breakthroughs in machine learning technology
Protected: Classification (4) Group learning(Ensemble Learning, Random Forest) and evaluation of learning results(Cross-validation method)
Algorithms for collective learning for data classification and evaluation of classification results (ensemble learning, bagging, boosting, random forests, cross-validation)
Protected: Classification (3) Probabilistic Discriminant Function(Logistic, Softmax Regression) and Local Learning(K-nearest neighbor method, kernel density estimation)
Probabilistic discriminant functions and local learning used in classifiers for data classification