AIMS AND OBJECTIVES OF THE COURSE: The aim of the course is to acquaint students with the basic types of neural networks and the related training rules, to use the apparatus of neural networks for adequate modeling of different objects and systems, to apply this knowledge in robotic systems with artificial intelligence, as well as to develop flexible skills for teamwork, for giving presentations, etc.
DESCRIPTION OF THE COURSE: Main topics: Mathematical logic, logical operators and functions, predicates; Mathematical proofs, arguments and rules for implication; Set theory, operations with sets; Relations, types, properties, relational databases, presentation; Functions, graphs, functions as a special case of relations, properties, inverse function, composition of functions; Boolean algebra, functions and expressions, logical gates, principles for synthesis of logic schemes; Graph theory, types, operations, representation, paths and loops in graphs, connectivity, reachability; Trees, species, properties, binary search trees, algorithms for finding the minimum spanning tree; Combinatorics, counting, basic principles of counting; Introduction to probability theory, experience, event, distributions, conditional probabilities, Baes' theorem; Algorithms, complexity of algorithms, Turing machine, computability, algorithmically unsolvable problems; Mathematical induction, recursion, recursive functions, definitions and algorithms; State machines, alphabets and strings, languages..
