Artificial Inteligence

Course Code: Ν2-6020
Weekly Duty: 4 (2Th + 2L)
Typical Semester: 6th
Course Category: Specialty Course

Learning Outcomes

The purpose of this course is to familiarize students with the theoretical space of artificial intelligence and its applications.

The aim of the course is that students acquire knowledge that will lead to practical application in areas such as knowledge representation, problem solving algorithms and inference techniques in rule-based systems and acquire academic knowledge on issues of concern today in the field of artificial intelligence, such as production trees, neural networks, genetic algorithms, intelligent agents and their applications.

More specifically, the learning objectives of the course are students, after completion of the course, be able to:

  • describe problems and represent relevant knowledge with formal methods
  • distinguish between blind and heuristic search algorithms and their coding in the context of solving problems
  • understand the different ways of knowledge representation
  • understand the structure and operation of expert systems
  • design and develop expert systems based on rules
  • recognize the different kinds of machine learning
  • describe the operation of machine learning systems, such as decision trees, neural networks and genetic algorithms
  • recognize the characteristics of intelligent agents and their applications.

Course Content

Introduction. Problem solving, Blind search algorithms, Heuristic search algorithms. Knowledge representation. Rule Based Systems. Machine learning (Decision trees, Neural networks, Genetic algorithms). Expert Systems engineering. Intelligent Agents. Applications of AI to Natural Language Processing, Vision, Planning and Robotics.

Practical work in the use of common lisp for developing search algorithms and clips for creating expert systems.

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Internationalisation I18n