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Artificial Intelligence and Natural Language Processing

  1. Database. Data storage, addressing of records. Indexing and hash for multiple attributes, bitmap indexes, dynamic hash. Query evaluation, transformation rules, statistics and estimates. Optimizing queries and schema. Transaction processing, outages and recovery. Similar search.
    PA152, PA128
  2. Computational logic. A resolving method in predicate logic. Refinement of the resolution and the Horne clause. SLD-resolutions. Computational model of the logic program. Prolog Language. Plain evidence in propositional, predicate and modal logic. Inductive inference in propositional and predicate logic. Multi-valued logic.
  3. Complexity and algorithms for difficult problems. Complexity classes, reduction, complete problems. Basic temporal and spatial complexity classes, relation of determinism and non-determinism. P vs NP problem. Approximate algorithms. Using the linear programming task to construct approximate algorithms. Random algorithms. Heuristic approaches.
    IA012, IA101
  4. Statistics. Descriptive statistics, functional and numerical characteristics of characters. Discrete and continuous random variables (NV), basic layout. Numeric characteristics of NV. The Central Limit Theorem. Point estimates, confidence intervals, statistical hypothesis testing, materiality level. Linear regression, total F-test, partial t-tests. Statistical methods and evaluation of experiments.
    MV011, MA012
  5. Games, modeling, simulation. Games and basic game strategy. Modeling, model types, computer models, simulations. Markov models. Feedback, balance. Modeling with agents, network modeling. Modeling of thinking, learning, evolution. Cellular automata, production systems.
    IV109, IV111, PA154, M7190
  6. Soft Computing Methods. Neural Networks. Genetic algorithms. Complex systems, natural complex systems, social insects. Iterative local search, simulated annealing, taboo search.
    PV021, IV109, PV205
  7. Searching and programming with restrictive conditions. Solve problems by searching for state space. Consistency and algorithms for binary and non-binary conditions. Tree search without / with consistency techniques. Modeling using limiting conditions, global conditions.
  8. Machine learning and knowledge mining. Classification, clustering, frequent patterns and association rules. Multirelational Learning. Preprocessing of data. Mining from text and web documents. Results validation methods.
    PV056, PA164, PA055
  9. Scheduling and planning. Graham's classification. Scheduling using control rules, limiting conditions, local search and math programming. Project planning, job planning.
  10. Natural language processing. Automatic morphological analysis. Recognition and generation of sentence structure, grammar, basic types of syntactic analysis. Semantic analysis of the sentence, logical analysis of natural language. Pragmatic plane, communication situation. Corpus, statistical and rule markings.
    PA153, PV122, IA161, PA164, PA154
  11. Methods of representation and knowledge extraction. Representation of knowledge through rules, frames, semantic networks. Deductive and inductive derivation. Forward and reverse chaining rules. Derived with indeterminacy.
    PA153, IA008