Introduction to Technical Intelligence. Categories of technical intelligence, supervised, unsupervised and semi-supervised learning methods. Methods with or without modeling. Probabilistic methods. Intelligent agents.
Introduction 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.
Introduction to artificial intelligence methods without modeling (stateless). Classification methods, k-means, DBSCAN, spectral clustering. Unsupervised learning methods using training (autoencoders, stacked autoencoders, deep learning).
Introduction 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.
Introduction 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
Introduction 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
Introduction to Knowledge Bases and Expert Systems. Symbolic representation of knowledge: objects, production rules, semantic networks, frameworks, tables.
Symbolic Inference Methods and Decision Control Procedures. Use and mechanism of production rules, correct, reverse and two-way reasoning, deep – and broad – research.
Representation and drawing conclusions with uncertain and inconclusive knowledge. Uncertain Reasoning, Fuzzy Logic, Probability Reasoning, Theory of Testimony.
Development 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.
Programming Languages and Expert Systems Development Tools. Types of Tools, Language or Tool Selection, Hardware Infrastructure for Expert Systems.
Examples of expert systems. Review of systems experiences in Earth Sciences.
Outcome: Not Provided
