Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2017/2018

Problem solving: State space search (BFS, DFS, DLS, IDS, BS, UCS, Backtracking, Forward checking, Min-conflict, BestFS, GS, A*, Hill Climbing, Simulated annealing methods). Solving of optimization problems using algorithms inspired by nature (GA, ACO and PSO). Problem decomposition (And Or graphs), games playing (Mini-Max and Alfa-Beta algorithms). AI languages (PROLOG, LISP) and implementations of basic search algorithms in these languages. Machine learning principles. Statistical and structural pattern recognition. Basic principles of expert systems. Fundamentals of computer vision. Base principles of natural language processing. Application fields of artificial intelligence.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

  • Students will learn terminology in Artificial Intelligence field both in Czech and in English language.
  • Students will learn read and so partly write logic and functional programs.

  • Students will acquaint with problem solving methods based on state space search and on decomposition problem into sub-problems.
  • Students will acquaint with basic game playing methods of two players.
  • Students will learn to solve optimization problems.
  • Students will acquaint with fundamentals of propositional and predicate logics and with their applications.
  • Students will learn how to use basic methods of machine learning.
  • Students will acquaint with fundamentals of machine vision and natural language processing.

Prerequisites

  • Basic knowledge of the programming in any procedural programming language.
  • Knowledge of secondary school level matematics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

At least 15 points earned during semester (mid-term test + programs in computer exercises).

Course curriculum

Syllabus of lectures:
  1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
  2. State space search methods.
  3. Solving methods using decomposition problems into sub-problems (AND/OR graphs).
  4. Solving of optimization problems using algorithms inspired by nature - short introduction into Genetic algorithms, ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization).
  5. Methods of game playing (minimax, alpha-beta, games with unpredictability).
  6. Logic and AI, resolution and it's application in problem solving.
  7. Implementation of basic search algorithms in PROLOG.
  8. Implementation of basic search algorithms in LISP.
  9. Machine learning.  
  10. Fundamentals of pattern recognition theory. Classical classifiers, perceptron.
  11. Expert systems.
  12. Principles of computer vision.
  13. Principles of natural language processing.

Syllabus of computer exercises:
  1. Problem solving - simple programs.
  2. Problem solving - games playing.
  3. PROLOG language - basic information.
  4. PROLOG language - simple individual programs.
  5. LISP language - basic information.
  6. LISP language - simple individual programs.
  7. Simple programs for pattern recognition.

Work placements

Not applicable.

Aims

To give the students the knowledge of fundamentals of artificial intelligence, namely knowledge of problem solving approaches, machine learning principles and general theory of recognition. Students acquire base information about expert systems, computer vision and natural language processing.

Specification of controlled education, way of implementation and compensation for absences

  • Mid-term written examination - 20 points.
  • Programs in computer exercises - 20 points.
  • Final written examination - 60 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7
  • Luger,G.F.: Artificial Intelligence - Structures and strategies for Complex Problem Solving, 6th Edition,
    Pearson Education, Inc., 2009, ISBN-13: 978-0-321-54589-3, ISBN-10: 0-321-54589-3 

Recommended reading

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

  1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
  2. State space search methods.
  3. Solving methods using decomposition problems into sub-problems (AND/OR graphs).
  4. Solving of optimization problems using algorithms inspired by nature - short introduction into Genetic algorithms, ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization).
  5. Methods of game playing (minimax, alpha-beta, games with unpredictability).
  6. Logic and AI, resolution and it's application in problem solving.
  7. Implementation of basic search algorithms in PROLOG.
  8. Implementation of basic search algorithms in LISP.
  9. Machine learning.  
  10. Fundamentals of pattern recognition theory. Classical classifiers, perceptron.
  11. Expert systems.
  12. Principles of computer vision.
  13. Principles of natural language processing.

Exercise in computer lab

13 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

  1. Problem solving - simple programs.
  2. Problem solving - games playing.
  3. PROLOG language - basic information.
  4. PROLOG language - simple individual programs.
  5. LISP language - basic information.
  6. LISP language - simple individual programs.
  7. Simple programs for pattern recognition.

E-learning texts

František V. Zbořil: Základy umělé inteligence (cs)