Title:

Fundamentals of Artificial Intelligence

Code:IZU
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeFieldYearDuty
IT-BC-3BIT2ndCompulsory
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/IZU/private/
Credits:4
Completion:credit+exam (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600130
 ExamsTestsExercisesLaboratoriesOther
Points:60202000
Guarantor:Zbořil František V., doc. Ing., CSc. (DITS)
Deputy guarantor:Zbořil František, doc. Ing., Ph.D. (DITS)
Lecturer:Zbořil František, doc. Ing., Ph.D. (DITS)
Zbořil František V., doc. Ing., CSc. (DITS)
Instructor:Puda Tomáš, Ing. (FIT)
Puda Tomáš, Ing. (DITS)
Rozman Jaroslav, Ing., Ph.D. (DITS)
Šoková Veronika, Ing. (DITS)
Šůstek Martin, Ing. (DITS)
Uhlíř Václav, Ing. et Ing. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Substitute for:
Artificial Intelligence (UIN), DITS
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
Moncomp.lablecturesN203 08:0017:50
Moncomp.lablecturesN204 10:0015:50
MonlecturelecturesE104 E105 E112 11:0012:502BIB 3BIT xx
TuelecturelecturesE104 E105 E112 13:0014:502BIA 3BIT xx
Tuecomp.lablecturesN204 N205 14:0017:50
Wedcomp.lablecturesN204 08:0017:50
Thucomp.lablecturesN204 08:0011:50
Fricomp.lablecturesN203 N205 08:0011:50
Fricomp.lablecturesN204 10:0015:50
 
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.
Description:
  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.
Knowledge and skills required for the course:
  
  • Basic knowledge of the programming in any procedural programming language.
  • Knowledge of secondary school level mathematics.
Subject specific learning outcomes and competencies:
  
  • Students will learn terminology in Artificial Intelligence field both in Czech and in English language.
  • Students will learn read and so partly write programs in PROLOG language.
Generic learning outcomes and competencies:
  
  • 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 expert systems, machine vision and natural language processing.
  • Students will acquaint with fundamentals of multiagent systems.
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 serious and very useful branch of computer science. Furthermore, students will learn basic techniques and approaches to solving problems that they can use then for creation of artificial intelligent systems.
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 into agent systems.
Syllabus of computer exercises:
 
  1. Problem solving - State Space Search.
  2. Problem solving - CSP.
  3. Problem solving - game playing.
  4. Predicate logic - method of resolution.
  5. PROLOG language - basic information.
  6. PROLOG language - simple individual programs.
  7. Simple programs for pattern recognition.
Fundamental 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
  • 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
Study 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
  • Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
Controlled instruction:
  Missed lessons (exercises and tests) can be substituted only exceptionally, after proving that the absences had legitimate reasons.
Progress assessment:
  
  • 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.
Exam prerequisites:
  At least 15 points earned during semester (mid-term test + programs in computer exercises).
 

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