ARTIFICIAL INTELLIGENCE

Type of course:	Compulsory
Language of instruction:	English
Erasmus Language of instruction:	English
Name of lecturer:	Maria Viorela Muntean
Seminar tutor:	Maria Viorela Muntean
Form of education	Full-time

Form of instruction:	Class
Number of teaching hours per semester:	42
Number of teaching hours per week:	3
Semester:	Autumn
Form of receiving a credit for a course:	Grade
Number of ECTS credits allocated	4

Course aims:

The course is a coherent introduction in Artificial Intelligence area, including theoretical and practical approaches.
The identification of appropriate models and methods for solving real-life problems.
The use of methodologies, specification mechanisms and development environments for the development of computer applications.
The use of computer and mathematical models and tools to solve specific problems in the application field.
Students will deal with the two AI approaches: symbolic and conexionist and they will use AI applications and languages.

Course Entry Requirements:

N/A

Course contents:

Introduction. Ai definitions. Short hystory of ai. Ai components Problem solving. Solving problems by searching. Uninformed search strategies. Informed (heuristic) search strategies Other problem solving strategies. Constraint satisfaction problems. Adversarial search (games) Knowledge representation Knowledge representation by rules Structured knowledge Uncertain knowledge and reasoning (fuzzy) Planning and learning in AI systems Artificial neural networks (ANN) foundations ANNs applications Expert Systems foundations Intelligent agents and robots.

Teaching methods:

Lecture, conversation, exemplification.

Learning outcomes:

The use of methodologies, specification mechanisms and development environments for the development of computer applications. The identification and explanation of base computer models that are suitable for the application domain. The use of computer and mathematical models and tools to solve specific problems in the application field. The identification of appropriate models and methods for solving real-life problems.

Learning outcomes verification and assessment criteria:

Written paper 50% Laboratory activities portfolio 50%