machine learning

アルゴリズム:Algorithms

Protected: Calculation of marginal probability distribution – Kikuchi approximation

Application of graphical models to stochastic generative models for digital transformation, artificial intelligence, and machine learning tasks; calculation of marginal probability distributions in the generalized stochastic propagation method with Kikuchi free energy functions and comparison with Bethe free energy functions and Hasse diagrams
アルゴリズム:Algorithms

Protected: Overview of Bayesian Estimation with Concrete Examples

Calculate the fundamentals of Bayesian estimation (exchangeability, de Finetti's theorem, conjugate prior distribution, posterior distribution, marginal likelihood, etc.) used in probabilistic generative models for digital transformation, artificial intelligence, and machine learning tasks, based on concrete examples (Dirichlet-multinomial distribution model, gamma-gaussian distribution model).
アルゴリズム:Algorithms

Protected: Overview of Gaussian Processes(4)Hyperparameter Estimation and Generalization of Gaussian Process Regression

Hyperparameter estimation using the gradient descent method of Gaussian process regression for stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks (SCG method, L-BFGS method, global solution using MCMC)
ICT技術:ICT Technology

Overview and installation of CentOS8

Overview and installation of CentOS, an open source operating system that is functionally compatible with RedHat, a commercial Linux ICT infrastructure used for digital transformation , artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Model Selection and Regularized Path Tracking (2)Regularized Path Tracking

On Regularized Path Tracking Algorithms in Model Validation for Improving Efficiency of SVMs Used in Digital Transformation, Artificial Intelligence , and Machine Learning Tasks
IOT技術:IOT Technology

Protected: Causal Inference with VAR Models (2)Multivariate Autoregressive (VAR) Models and Causal Inference with VAR Models

Multivariate autoregressive models (VAR models) and causal estimation using VARs in time series data analysis with state space models utilized in digital transformation, artificial intelligence and machine learning tasks
アルゴリズム:Algorithms

Protected: Computing the Peripheral Probability Distribution 2 – Bethe Approximation

Variational methods using the Bethe approximation to compute marginal probability distributions in probability propagation methods for probability estimation using graphical models utilized in digital transformation, artificial intelligence, and machine learning tasks.
アルゴリズム:Algorithms

Protected: Overview of Stochastic Generative Models and Learning

Probabilistic generative models used in digital transformation , artificial intelligence and machine learning , overview of graphical models and maximum likelihood methods, MAP estimation, Bayesian estimation and Gibbs sampling.
アルゴリズム:Algorithms

Protected: Overview of Gaussian Processes(3)Gaussian Process Regression Model

Computation and optimization of regression models and predictive distributions using Gaussian processes, which are dimensionless stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Model selection and regularization path tracking (1) Cross-validation method

Cross-validation methods (k-partition cross-validation and one-out cross-validation) for selecting hyper-parameters such as regularization parameters for support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks
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