13D111VIE - Artificial Intelligence and Expert Systems

Course specification
Course title Artificial Intelligence and Expert Systems
Acronym 13D111VIE
Study programme Electrical Engineering and Computing
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      The goal Introduce students to the methodologies and applications of modern development in artificial intelligence area, with an emphasis on advanced technologies.
      The outcome The learning outcomes of the course are to enable students to implement various concepts and technologies in artificial intelligence applications. They are able to identify characteristics of the system and select the best and most effective solutions.
      Contents of lectures Knowledge and reasoning - BPN, Rules, semantics and reasoning in Bayes's network; decision support - the basis of the theory model; working with multiple agents, game theory. Machine learning - forms of learning, decision trees, machine learning theory. Effective interpretation, search and processing of a data set. NLP - semantic similarity, sentiment.
      Contents of exercises
      1. S. Russel, P. Norvig - Artificial Intelligence - A Modern Approach (Original title)
      2. Daniel Jurafsky, James H. Martin: Speech and Language Processing (Original title)
      3. Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      Methods of teaching Presentation, individual work, discussion
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper 0
      Practical lessons 0 Oral examination 30
      Projects 70
      Colloquia 0
      Seminars 0