Course details

Fundamentals of Artificial Intelligence

IZU Acad. year 2020/2021 Summer semester 4 credits

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 optimization problems by nature-inspired algorithms (GA, ACO and PSO). Problem decomposition (And Or graphs), games playing (Mini-Max and Alfa-Beta algorithms). AI language PROLOG and implementations of basic search algorithms in this language. 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.

Guarantor

Deputy Guarantor

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 13 hrs projects

Assessment points

70 exam, 30 projects

Department

Lecturer

Instructor

Course Web Pages

Subject specific learning outcomes and competences

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

Generic learning outcomes and competences

  • 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 logic and with their applications.
  • Students will learn how to use basic methods of machine learning.
  • Students will acquaint with fundamentals of expert systems, machine vision and natural language processing.
  • Students will acquaint with fundamentals of multiagent systems.

Learning objectives

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.

Why is the course taught

In the IZU course, students should gain knowledge what artificial intelligence is, realize that the artificial intelligence does not mean artificial being, but that it is a serious and very useful branch of computer science. Furthermore, students will learn basic techniques and approaches to solving problems that they can use them for the creation of artificially intelligent systems.

Prerequisite kwnowledge and skills

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

Study literature

  • Russell,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
  • Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310

Fundamental literature

  • Russell,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
  • Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
  • Pool, D. L., Mackworth, A. K.: Artificial Intelligence, Cambridge University Press, 2010,  ISBN-13 978-0-521-51900-7

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.
  4. Solving optimization problems using algorithms inspired by nature.
  5. Methods of game playing (two players).
  6. Logic and AI, resolution and it's application in problem-solving and planning.
  7. PROLOG language and its use in AI.
  8. Machine learning.  
  9. Pattern recognition.
  10. Principles of expert systems.
  11. Principles of computer vision.
  12. Principles of natural language processing.
  13. Introduction to agent systems.

Syllabus - others, projects and individual work of students

  • Project dealing with state space search and game playing
  • Project dealing with logic and PROLOG language
  • Projects dealing with machine learning 

Progress assessment

  • Projects (homeworks) - 30 points.
  • Final written examination - 70 points; The minimal number of points which can be obtained from the final written examination is 28. Otherwise, no points will be assigned to a student.

Controlled instruction

Four projects during the semester.

Exam prerequisites

At least one point per project. At least ten points from all projects together.

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Monlecturelectures E104 E105 E112 11:0012:50 2BIB 3BIT xx
Tuelecturelectures E104 E105 E112 13:0014:50 2BIA 3BIT xx
Wedexam2021-05-12 A112 A113 A218 C114 C123 C126 C209 C228 D0206 D0207 D105 E104 E105 E112 G108 G202 L314 P209 Q204 Q304 09:0010:50 2BIA 2BIB 3BIT řádná
Wedexam2021-05-12 E105 11:0012:50 2BIA 2BIB řádná
Thuexam2021-06-03 A112 A113 C126 C127 C228 E104 E105 E112 G108 G202 L314 P108 P209 Q204 R211 R212 12:0013:50 2BIA 2BIB 3BIT 1. oprava
Friexam2021-06-11 A113 A218 C123 C126 C127 E104 E105 E112 G108 G202 P209 Q204 R211 R212 15:0016:50 2BIA 2BIB 3BIT 2. oprava

Course inclusion in study plans

  • Programme BIT, 2nd year of study, Compulsory
  • Programme IT-BC-3, field BIT, 2nd year of study, Compulsory
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