推論技術:inference Technology Protected: Modeling of time series and spatial data (1)(Dynamic linear model)
Bayesian modeling of temporal and spatial models with a focus on dynamic linear models and evaluation using MCMC
数学:Mathematics
推論技術:inference Technology IOT技術:IOT Technology Protected: Model-based reinforcement learning(Sparse sampling, UCT, Monte Carlo search tree)
Model-based reinforcement learning (sparse sampling, UCT, Monte Carlo search trees) used for digital transformation artificial intelligence , and machine learning tasks.
グラフ理論 Structural Learning
Structural Learning
Overview
Learning the structure that data has is important for interpreting what the data ...
微分積分:Calculus Machine Learning Professional Series “Continuous Optimization for Machine Learning” Reading Memo
Summary
Continuous optimization in machine learning is a method for solving optimization problems in which varia...
IOT技術:IOT Technology Protected: Model-free reinforcement learning (2) – Method iteration (Q-learning, SARSA, Actor-click method)
Value iteration methods Q-learning, SARSA, Actor-critic methods to model-free reinforcement learning for digital transformation , artificial intelligence and machine learning tasks.
オンライン学習 Protected: Trade-off between exploration and utilization -Regret and stochastic optimal measures, heuristics
Reinforcement learning with regrets, stochastic optimal measures, and heuristics
IOT技術:IOT Technology Time series data analysis
Time Series Data Learning
Overview
Time-series data is called data whose values change over time, such ...
オンライン学習 Protected: Planning Problems (2) Implementation of Dynamic Programming (Value Iterative Method and Measure Iterative Method)
Implementation of Dynamic Programming (Value Iteration and Policy Iteration) for Planning Problems as Reinforcement Learning for Digital Transformation , Artificial Intelligence and Machine Learning Tasks
推論技術:inference Technology Statistical Causal Inference and Causal Search
Statistical Causal Inference and Causal Search
Overview
When using machine learning, it is important to consider...
強化学習 Protected: Planning Problems(1) – Approaches Using Dynamic Programming and Theoretical Underpinnings
Reinforcement learning by planning problems (dynamic programming and linear programming) for sequential decision problems in known environments used for digital transformation , artificial intelligence and machine learning tasks.