machine learning

Protected: Causal Inference with VAR Model (1) Interpolation of missing data and DF and ADF tests

Overview of multivariate autoregressive models for finding causal relationships between two time series data in time series data analysis using state space models for digital transformation, artificial intelligence, and machine learning tasks, and completion of missing data using R and DF and ADF tests

2022.07.07

IOT技術:IOT TechnologyStream Data Processing微分積分:Calculus推論技術:inference Technology時系列データ解析最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Calculation of marginal probability distributions – Probability Propagation Method

Compute the probability distribution around graphical models in probabilistic generative models used in digital transformation, artificial intelligence , and machine learning tasks, such as Bayesian estimation, using probability propagation methods

2022.07.06

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Various probability distributions

Overview of various probabilistic models used as approximate models for probabilistic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (Student's t distribution, Wishart distribution, Gaussian distribution, gamma distribution, inverse gamma distribution, Dirichlet distribution, beta distribution, categorical distribution, Poisson distribution, Bernoulli distribution)

2022.07.05

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overview of Gaussian Processes(2)Gaussian Processes and Kernels

Gaussian processes and kernel functions (Mattern kernel, character kernel, Fisher kernel, HMM's marginalized kernel, linear kernel, exponential kernel, periodic kernel, RBF kernel), which are dimensionless stochastic generative models used for digital transformation, artificial intelligence and machine learning tasks

2022.07.04

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Partitioning Methods in Support Vector Machines (2) DCDM Algorithm for Linear SVM

DCDM algorithm (dual coordinate descent method algorithm), an efficient algorithm for processing large amounts of (sparse) data on support vector machines (algorithm for linear SVM used in LIBLINEAR) used in digital transformation (DX), artificial intelligence (AI) and machine learning (ML) tasks.

2022.07.01

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Applications of State Space Models to Marketing

Application to marketing using evolution and evolution in time-series data analysis using state-space models utilized in digital transformation, artificial intelligence, and machine learning tasks.

2022.06.30

IOT技術:IOT TechnologyRStream Data Processing時系列データ解析機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Graphical model with factor graph representation

An overview of factor graph models, a more generalized version of graphical models used in probabilistic generative models for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.06.29

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Non-parametric Bayesian and clustering(1)Dirichlet distribution and infinite mixture Gaussian model

Analysis with a mixed Gaussian model that extends the Dirichlet distribution to infinite dimensions as a nonparametric Bayesian approach in stochastic generative models used in digital transformation artificial intelligence, and machine learning

2022.06.28

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Overview of Gaussian Processes (1) Gaussian Processes and Kernel Tricks

Overview of the theory of Gaussian processes (support vector machines, related vector machines, RVMs, RBF kernels, kernel functions), which are dimensionless multivariate Gaussian distribution models with no specified parameters among stochastic generative models used in digital transformation, artificial intelligenceand machine learningtasks.

2022.06.27

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Partitioning Method on Support Vector Machines (1) SMO Algorithm

Efficiency using the partitioning method (SMO algorithm) on support vector machines utilized for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.06.24

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus最適化:Optimization機械学習:Machine Learning画像認識技術確率・統計:Probability and Statistics自然言語処理:Natural Language Processing音声信号認識技術