Artificial Intelligence

Studijní plán: Aplikovaná informatika - platný pro studenty, kteří započali studium od ZS 2019/2020

PředmětArtificial Intelligence (UIa-1)
GarantujeKatedra technických studií (KTS)
Garantdoc. Dr. Ing. Jan Voráček, CSc. ( )
Počet kreditů4
Prezenční studium
Přednáška2 h
Cvičení2 h
Kombinované studium
Tutoriál / přednáška4 h
Cvičení8 h
Studijní plán Typ Sem. Kred. Ukon.
Aplikovaná informatika - kombinovaná forma, platný pro studenty, kteří započali studium od ZS 2019/2020 PV 6 4 kr. Z,ZK
Aplikovaná informatika - platný pro studenty, kteří započali studium od ZS 2019/2020 PV 6 4 kr. Z,ZK


  • Artificial intelligence – definition, history, areas and terminology.
  • Solving problems by state space searching.
  • Knowledge, its elicitation and representation (logic, frames, rules).
  • Expert/knowledge-based systems.
  • Alternative ways of uncertainty processing, Bayesian approach.
  • Machine learning: structural representation and parametrisation. Supervised and unsupervised learning. Interpretation of learning outcomes.
  • Data mining: principles and applications.
  • Introduction into soft-computing (non-derivative optimization methods, neural networks, and fuzzy logic).
  • Fundamentals of computer speech and image processing.

Doporučená literatura

  • ŠTĚPÁNKOVÁ, O. MAŘÍK, V. LAŽANSKÝ, J. a kolektiv, Umělá inteligence 1-6. Praha: Academia, 2003 – 2013 (vybrané kapitoly).
  • RUSSELL, S. J., NORVIG, P. Artificial Intelligence: A Modern Approach. 3. vyd. Pearson, 2009, 1152 s. ISBN 978-0136042594.
  • WITTEN, I., EIBE, F., HALL, M., PAL, C. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. Elsevier, 2016, 65 s. ISBN: 978-0128042915.


The goal of the course is to familiarise students with the possibilities of applying artificial intelligence instruments and techniques in real-life problem solving. The student is able to use the knowledge and skills to support planning, managing and decision-making especially in the area of technical, social and natural sciences. They can distinguish directly algorithmized tasks from computationally complex, typically NP-complete tasks, which need supplementary information to find results in a feasible time (heuristics, utility functions etc.)

Knowledge: The student knows fundamental methods of uninformed and informed state space search, they can represent both definite and indefinite knowledge in various ways and on its basis they can derive and validate new knowledge. They comprehend the issue of machine learning on both linear and nonlinear structures (trees, neural networks). They can find sub-optimal solution of NP-complete problems in a finite time. They can solve application tasks by means of fuzzy logic.

Skills: The student understands artificial intelligence trends and can use this knowledge in practice. They can formulate applicable heuristics and criterial functions. They work with the general concept of the intelligent agent and they can adjust it to a particular assignment. They realize the significance of input data quality and sufficiency. They interpret achieved results correctly and consider their limitations.

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