Artificial Intelligence

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Code	Completion	Credits	Range	Language
2312014	KZ	2	2P+0C	Czech

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Department of Mechanics, Biomechanics and Mechatronics

Synopsis:

Tasks and challenges of Artificial Intelligence (AI), history of AI, Turing?s, Minsk?s and Kotek?s definitions of AI, approaches of mathematical logic. SW and HW tools for rapid prototyping and implementation of AI, commercial vs. open-source tools, modern AI trends (CUDA, neuromorphic HW, cloud technologies). Production systems, knowledge representation and expert systems, recommender systems. AI based on machine learning and data processing, an overview of computational intelligence tools for AI. Classification, decision trees, tree algorithms, gradient boosting. Neural Networks (NS), division, basic concepts and principles, shallow vs. deep NS (Deep Learning). Incremental and batch supervised machine learning algorithms, (Levenberg-Marquardt, Conjugate Gradients, Entropy Criterion Functions). Linear and Polynomial Neural Architectures, MLP, Extreme Machine Learning, Echo State NS. Data clustering, self-organizing maps (SOM). Dimensionality Reduction, PCA, Auto Encoder, Convolution Network, Deep Learning, Deep Neural Networks. Indefinite information and uncertainties in data. Fuzzy logic and fuzzy sets, fuzzy rule systems, neuro-fuzzy systems. Type-2 fuzzy sets, Type-2 uncertainty in relation to the data measurement.

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