微分積分:Calculus

アルゴリズム:Algorithms

Protected: Stochastic Generative Models and Gaussian Processes(3) Representation of Probability Distributions

Stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks and representation of probability distributions in samples as a basis for Gaussian processes ,weighted sampling, kernel density estimation, distribution estimation using neural nets
Clojure

Implementation of a Bayesian optimization tool using Clojure

Introduction of Clojure implementation of Bayesian optimization tool, a (hyperparameter) optimization tool used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks, and opimx, an optimization comparison tool in R.
Clojure

Protected: Implementation of a simple anomaly detection algorithm using Clojure

Implementation of simple anomaly detection algorithms (establishment density functions; PDF-based models) using Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
アルゴリズム:Algorithms

Protected: Support Vector Machines for Weak Label Learning (2) Multi-Instance Learning

Extension of support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks; multi-instance learning approach with SVMs for weak-label learning problems (mi-SVM, MI-SVM)
アルゴリズム:Algorithms

Protected: Computation of graphical models with hidden variables

Parameter learning of graphical models with hidden variables using variational EM algorithm in stochastic generative models (wake-sleep algorithm, MCEM algorithm, stochastic EM algorithm, Gibbs sampling, contrastive divergence method, constrained Boltzmann machine, EM algorithm, KL divergence)
アルゴリズム:Algorithms

Protected: Application of Variational Bayesian Algorithm to Matrix Decomposition Models with Missing Values

Application of variational Bayesian algorithm to matrix factorization models with missing values as a stochastic generative model computation for use in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Application of Nonparametric Bayesian Structural Change Estimation

Nonparametric Bayesian structural change estimation using Gibbs sampling as an application of probabilistic generative models for digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Stochastic Generative Models and Gaussian Processes(2)Maximum Likelihood and Bayesian Estimation

Maximum Likelihood and Bayesian Estimation Overview for Probabilistic Generative Models and Gaussian Process Fundamentals Used in Digital Transformation, Artificial Intelligence, and Machine Learning Tasks
python

GPy – A Python-based framework for Gaussian processes

GPy Gaussian regression problem, auxiliary variable method, sparse Gaussian regression, Bayesian GPLVM, latent variable model with Gaussian processes, a Python-based implementation of Gaussian processes, an application of stochastic generative models used in digital transformation, artificial intelligence and machine learning tasks.
Clojure

Implementation of Gaussian Processes in Clojure

Implementation of Gaussian processes in Clojure using fastmath as an extension of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks
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