python Python and Machine Learning
Overview of Python, a programming language used in digital transformation , artificial intelligence , and machine learning
自然言語処理:Natural Language Processing
python 推論技術:inference Technology Protected: Introduction to Hierarchical Bayes (from GLM to Hierarchical Bayesian Model)
Bayesian inference that can be used for artificial intelligence (AI), natural language, and digital transformation (DX); generate hierarchical Bayesian models from GLM models to solve complex statistical models
機械学習:Machine Learning Deep Learning Technologies
Deep learning technology, one of the breakthroughs in artificial intelligence technology and machine learning technology
推論技術:inference Technology Overview of Kernel Methods and Support Vector Machines
On kernel methods, one of the breakthroughs in machine learning technology
推論技術:inference Technology Protected: Classification (4) Group learning(Ensemble Learning, Random Forest) and evaluation of learning results(Cross-validation method)
Algorithms for collective learning for data classification and evaluation of classification results (ensemble learning, bagging, boosting, random forests, cross-validation)
R R language and Machine Learning
Overview of the R language as a general-purpose tool for machine learning
機械学習:Machine Learning Protected: Two approaches to language meaning (fusion of symbolic and distributed representations)
Symbolic and vector representation approaches to natural language meaning that can be used for artificial intelligence (AI) and digital transformation (DX) tasks and their integration, content relation recognition, paraphrase recognition, semantic similarity recognition, datasets (RTE, RITE, STS)
機械学習:Machine Learning Protected: Teaching the meaning of words to a computer (on various language models)
Meaning of Natural Language by WordNet (Dictionary), Distributed Hypothesis, PPMI, Singularity Decomposition (SVD), Word2Vec (Distributed Representation) in Natural Language Processing
機械学習:Machine Learning Protected: Topic models that capture the individuality of language
Topic models to capture latent meanings behind sentences, differences between various probabilistic approaches and deep learning, supervised LDA, Boltzmann machines,Naive Bayes
確率・統計:Probability and Statistics Introduction to models of language (probabilistic unigram models and Bayesian probability)
Natural language processing as it applies to digital transformation (DX), artificial intelligence (AI), machine learning, etc. Modeling of natural language, application of unigram models and Bayesian probabilistic models.