グラフ理論 Optimization for the First Time Reading Notes
Optimization for the First Time Reading Notes
From Optimization for Beginners
This book is explained in d...
グラフ理論
グラフ理論 アルゴリズム:Algorithms Protected: About Bayesian Statistics and Machine Learning
The impact of machine learning on scientific methodology and engineering, and the suitability of probabilistic statistical approaches, particularly Bayesian models, for machine learning design
アルゴリズム:Algorithms The Impact of Blockchain: A Disruptive Technology that is Overturning the Social Structure from Bitcoin, FinTech to IoT – Reading Notes
Mathematics Machine Learning Technology Artificial Intelligence Technology Algorithm Digital Transformation Technolo...
アルゴリズム:Algorithms Protected: Computing Peripheral Probability Distributions – Mean Field Approximation
Application of graphical models to stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks; approximate computation and algorithms for peripheral probability distributions from variational problems using mean field approximation
アルゴリズム:Algorithms Protected: Comparison of clustering using k-means and Bayesian estimation methods (mixed Gaussian model)
Comparison of k-means and Bayesian estimation (mixed Gaussian model) clustering as probabilistic generative models utilized in digital transformation, artificial intelligence , and machine learning tasks
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)
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 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