. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Artificial intelligence and knowledge base"@en . . "7.5" . "Introduction to Technical Intelligence. Categories of technical intelligence, supervised, unsupervised and semi-supervised learning methods. Methods with or without modeling. Probabilistic methods. Intelligent agents.\r\nIntroduction to artificial intelligence methods without modeling (stateless - Supervised learning). The structure of the simple preceptor, neuron. The structure of neural networks. The method of backpropagation. Syneclectic deep learning neural networks. Nonlinear categorization methods.\r\nIntroduction to artificial intelligence methods without modeling (stateless). Classification methods, k-means, DBSCAN, spectral clustering. Unsupervised learning methods using training (autoencoders, stacked autoencoders, deep learning).\r\nIntroduction to artificial intelligence methods with modeling (state modeling - deterministic). Introduction to Search Problem Modeling. Search trees. Heuristic methods. Local Search Algorithms and Optimization Methods. Search by width, depth. Uniform Cost Search. A Star A Star Relaxations.\r\nIntroduction to artificial intelligence methods with state modeling. Competitive methods. Game Theory, Max Min Algorithms, ExpectMax Algorithms, Alpha-Beta pruning. Adversarial Generative Networks (GANs) and deep learning\r\nIntroduction to artificial intelligence methods with state modeling. Bayesian classifiers, Decision tress, modeling with Markov models, policy evaluation, particle filters, Q-learning, Reinforcement learning, deep reinforcement learning\r\nIntroduction to Knowledge Bases and Expert Systems. Symbolic representation of knowledge: objects, production rules, semantic networks, frameworks, tables.\r\nSymbolic Inference Methods and Decision Control Procedures. Use and mechanism of production rules, correct, reverse and two-way reasoning, deep – and broad – research.\r\nRepresentation and drawing conclusions with uncertain and inconclusive knowledge. Uncertain Reasoning, Fuzzy Logic, Probability Reasoning, Theory of Testimony.\r\nDevelopment of experienced systems. The Architecture of Experienced Systems. Steps to Develop an Expert System. Formulation and Identification of the Problem. Conceptual Conception of the Problem, Capture of knowledge from written sources and Experts. Standardization and Organization of the Knowledge Base, Implementation of the Expert System, Evaluation of the Expert System.\r\nProgramming Languages and Expert Systems Development Tools. Types of Tools, Language or Tool Selection, Hardware Infrastructure for Expert Systems.\r\nExamples of expert systems. Review of systems experiences in Earth Sciences.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Master in Geoinformatics"@en . . "http://geoinformatics.ntua.gr/ https://www.survey.ntua.gr/en/postgrad/geoinformatics" . "90"^^ . "Presential"@en . "The Interdisciplinary PostGraduate Programme \"GeoInformatics\" is offered by NTUA since 1998, with the participation of: (a) School of Rural and Surveying Engineering, (b) School of Electrical and Computer Engineering, and (c) School of Mining and Metallurgical Engineering. The general coordination and support of the program as well as the awarding of the degree is undertaken by the School of Rural and Surveying Engineering. The Programme is addressed to NTUA graduates but also to those from other universities, having an interest in geospatial science and technology. It provides specialization in: (a) the collection, georeferencing, description, interpretation, and mapping of geospatial data of the physical, artificial and socio-economic environment, (b) spatial analysis and planning, and (c) the development of various geo-applications employing state-of-the-art methods and cutting-edge geospatial technologies. The duration of the Programme is three academic semesters. Awarding the Postgraduate Specialization Diploma (MSc) in Geoinformatics requires: (a) successful completion of 8 courses (4 core and 4 specialization courses), and (b) successful completion of a research thesis. The total number of credits (ECTS) corresponding to the acquisition of the Diploma is 90 (30 per semester). The number of annually enrolled students is about 30. The number of applicants varies between 70 and 120."@en . . . "1,5 years"@en . "TRUE" . . "Master"@en . "Thesis" . "no tuition, other costs may apply" . "Euro"@en . "1000.00" . "None" . "No Job Prospects Listed"@en . "2"^^ . "FALSE" . "Downstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . .