確率・統計:Probability and Statistics

アルゴリズム:Algorithms

Protected: Model Building and Inference in Bayesian Inference – Overview and Models of Hidden Markov Models

Model building and inference of Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks - Overview of hidden Markov models and models eigenvalues, hyperparameters, conjugate prior, gamma prior, sequence analysis, gamma distribution, Poisson distribution, mixture models graphical model, simultaneous distribution, transition probability matrix, latent variable, categorical distribution, Dirichlet distribution, state transition diagram, Markov chain, initial probability, state series, sensor data, network logs, speech recognition, natural language processing
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Overview of Causal Forest and examples of application and implementation in R and Python

  Causal Forest Causal Forest is a machine learning model for estimating causal effects from observed d...
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Overview of graph neural networks and examples of application and implementation in python

Graph Neural Networks A graph neural network (GNN) is a type of neural network for data with a graph struc...
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Protected: Neural Networks as Applied Models of Bayesian Inference

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Protected: Logistic regression as an applied model of Bayesian inference

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Protected: Tensor Decomposition and Recommendation as Applied Models of Bayesian Inference

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Protected: Inference by Gibbs sampling in a topic model as an applied model of Bayesian inference.

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Protected: Overview of the topic model as an applied model of Bayesian inference and application of variational inference

Overview of topic models as applied Bayesian inference models for digital transformation, artificial intelligence, and machine learning tasks and application of variational inference variational inference algorithms, Dirichlet distribution, categorical distribution, LDA, topic models in multimedia
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Protected: Hidden Markov model building and structured variational inference in Bayesian inference

Hidden Markov model building and structured variational inference (mini-batch, structured variational inference, fully decomposed variational inference, additional learning, underflow, message passing, exact inference algorithms, forward-backward algorithms, approximate distribution of parameters) in Bayesian inference for digital transformation, artificial intelligence, machine learning tasks.
アルゴリズム:Algorithms

Protected: Reinforcement learning application areas (2)

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