State Space Model with Clojure: Implementation of Kalman Filter
State space models using Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks: implementation of Kalman filter
Protected: Analysis of time series data using Clojure
Analysis of time series data such as AR, MA, ARMA, etc. using Clojure for digital transformation, artificial intelligence machine learning tasks ACF, PACF, Partial Autocorrelation, Durbin-Levinson algorithm, autocovariance, moving average models, autocorrelation models, hybrid, random walk, discrete-time models
Analysis in R and Clojure using Stan for Markov Chain Monte Carlo (MCMC) models
Implementation using R and Clojure of Stan, a computational tool using MCMC for Bayesian estimation used in digital transformation, artificial intelligence, and machine learning tasks.
Java, Scala and Koltlin, general-purpose application building environments
Java, Scala and Koltlin, general-purpose application building environments used for digital transformation (DX), artificial intelligence (AI) and machine learning (ML) tasks
Protected: Machine Translation Today and Tomorrow – Different Machine Learning Approaches for Natural Language
Present and future of machine translation for digital transformation, artificial intelligence, and machine learning tasks - Different machine learning approaches for natural language machine translation based on attentional neural nets, machine translation based on encoding and decoding, recurrent neural nets, machine translation based on neural nets and neural models, neural net-based translation, tree-to-strong translation, pre-ordering, parse trees and parsing, word mapping, phrase-based translation
Protected: Overview of model-based approach to reinforcement learning and its implementation in python
Overview of reinforcement learning with model-based approaches used for digital transformation, artificial intelligence, and machine learning tasks and its implementation in python Bellman Equation, Value Iteration, Policy Iteration
Protected: Bayesian Learning and Conjugacy
Conjugacy of various probability functions (Gaussian, Bernoulli, Poisson, Dirichlet, and Gamma distributions) and prior distributions for Bayesian learning calculations in stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks
Protected: A linear summation method and message propagation algorithm for MAP estimation of discrete-state graphical models
MAP estimation using linear programming in a graphical model of discrete states in a stochastic generative model (max-sum diffusion (MSD) algorithm, Generalized MPLP, MPLP algorithm, dual solution of the relaxation problem, dual decomposition, solution by message propagation, separation algorithm, cycle inequality, MAP estimation problem formulated as a linear programming problem)
Protected: Nonparametric Bayes from the viewpoint of point processes – Poisson and gamma processes
Nonparametric Bayes from the viewpoint of point processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks - Poisson and gamma processes additive processes, Poisson random measures, gamma random measures, discreteness, Laplace functional, point processes
Protected: Computational Methods for Gaussian Processes(2)Variational Bayesian Method and Stochastic Gradient Method
Calculations using variational Bayesian and stochastic gradient methods for Gaussian process models, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learning tasks Kullback-Leibler information content, Jensen inequality, evidence lower bound function, mini-batch method, evidence lower bound, variational posterior distribution, evidence variational lower bound