Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2019/2020

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.

Learning outcomes of the course unit

  • 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.

  • 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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • 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 10 points earned during the semester (mid-term test + projects).

Language of instruction

Czech, English

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

Missed
lessons (exercises and tests) can be substituted only exceptionally, after proving
that the absences had legitimate reasons.

Classification of course in study plans

  • Programme BIT Bachelor's, 2. year of study, summer semester, 4 credits, compulsory

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, summer semester, 4 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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.
  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.

Exercise in computer lab

13 hours, compulsory

Teacher / Lecturer

Syllabus

  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.

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