Title:

Fundamentals of Artificial Intelligence

Code:IZU
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
BIT-2ndCompulsory
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:Kočí Radek, Ing., Ph.D. (DITS)
Zbořil František, doc. Ing., Ph.D. (DITS)
Zbořil František V., doc. Ing., CSc. (DITS)
Instructor:Martiček Štefan, Ing. (DITS)
Rozman Jaroslav, Ing., Ph.D. (DITS)
Šoková Veronika, Ing. (DITS)
Šůstek Martin, Ing. (DITS)
Tinka Jan, 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
Tueexam - řádná2019-05-14D0206 D105 09:0011:502BIA 2BIB 3BIT
Tueexam - 2. oprava2019-06-04E112 11:0013:502BIA 2BIB 3BIT
Friexam - 1. oprava2019-05-24D0206 D105 08:0010:502BIA 2BIB 3BIT
 
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). Problem decomposition (AND/OR graphs). Solving optimization problems by nature-inspired algorithms (GA, ACO and PSO). Games playing (Mini-Max and Alfa-Beta algorithms). Logic and artificial intelligence (method of resolution and its utilization for task solving and planning). PROLOG language and implementations of basic search algorithms in this language. Machine learning principles. Classification and patterns recognition. Basic principles of expert systems. Fundamentals of computer vision.  Principles of natural language processing. Introduction into agent systems.
Knowledge and skills required for the course:
  
  • Basic knowledge of the programming.
  • 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.
  • 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, classification and recognition.
  • 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 acquire basic knowledge of artificial intelligence and realize that artificial intelligence does not mean an artificial entity, but that it is a serious and very useful branch of computer science. Students will learn the basics of machine learning and problem solving approaches, which can then be used to design and create 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. Basic methods of game playing.
  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. Classification and 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 + tasks in computer exercises).
 

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