Introduction to Artificial Intelligence (FRI)

Higher education teachers: Bosnić Zoran
Credits: 6
Semester: winter
Subject code: 63214

Subject description

Content (Syllabus outline):

  • Introduction to Artificial Intelligence, examples of applications
  • State space and basic search algorithms: depth-first, breadth-first and iterative deepening, complexity of these algorithms • Heuristic search, algorithms A* and IDA*, admissibility theorem for A*, properties of heuristic function and analysis of time and space complexity
  • Problem decomposition with AND/OR graphs, search in AND/OR graphs, heuristic search algorithm AO*
  • Machine learning: problem of learning from data, data mining, description languages and hypothesis spaces, induction of decision trees, regression trees, model trees, and rules. Software tools for machine learning and applications.
  • Knowledge representation and expert systems: knowledge representation with rules, frames, semantic networks, ontologies; inference algorithms and generationg explanation; handling uncertain knowledge, Bayesian networks
  • Means-ends planning, total-order and partial-order planning, goal regression, applications in robotics and logistics

Objectives and competences:

Teach basic concepts, ideas, methods and techniques of artificial intelligence (AI) Ability to solve problems with methods of artificial intelligence Ability to understand the literature in the area of AI Contribute to the understanding of the relevance of technical achievements of AI with respect to their implications in philosophy and psychology

Intended learning outcomes:

After the completion of the course the student will be able to: - know the basic and most commonly used methods in the field of artificial intelligence (AI), - explain implications of the AI achievements, and its relation with cognitive science, psychology, medicine, logic, mathematics and other related fields, - define the technical boundaries of the field, - use the search and machine learning algorithms on real problems, - compare time and spatial complexity of the taught algorithms, - formulate selected problems from the real world as problems that are solvable with the AI algorithms.

Learning and teaching methods:

Lectures, laboratory exercises, homework, individual and team projects.

Study materials

Bratko, Prolog Programming for Artificial Intelligence, 4th edition, Pearson Education
Addison-Wesley 2011, ISBN: 0201403757
S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Third edition, Pearson Education, Prentice-Hall 2010, ISBN: 0136042597

Study in which the course is carried out

  • 3 year - 1st cycle - Multimedia