Artificial Intelligence
| Course Code | 321-3600 |
|---|---|
| Semester | 6 |
| ECTS | 5.00 |
| Hours (Theory) | 3 |
| Hours (Lab) | 2 |
| Instructor | Stamatatos Efstathios |
Course Content
Intelligent agents (basic concepts). Search in a state space for problem solving: Blind (but systematic) search, Guided search and heuristic methods, Search cost, Local search. Constraint satisfaction problems: Basic principles and algorithms. Planning: Basic principles and algorithms, Hierarchical planning. Machine learning: Introduction, Inductive learning, Machine learning algorithms.
Learning Outcomes
On completion of this module, students are expected to be able:
- To have the knowledge of defining an intelligent agent and familiarity with the types of intelligent agents.
- To have the ability to represent a problem so that it can be solved via state space search. Familiarity with blind search algorithms. Familiarity with heuristic search algorithms.
- To posses the Understanding of the properties of heuristic functions. Familiarity with local search algorithms.
- To have the ability to represent a problem as a constraint satisfaction problem. Familiarity with algorithms of solving constraint satisfaction problems.
- To posses knownledge of planning methods and understanding the algorithm of partial-order planning. Familiarity with the basic principles and algorithms of machine learning.
- To have the capacity of developing programs that use artificial intelligence algorithms.
Prerequisites
Not required.
Teaching and Learning Methods
| Activity | Semester workload |
|---|---|
| Lectures | 52 hours |
| Laboratory hours | 26 hours |
| Personal study | 44 hours |
| Final exams | 3 hours |
| Course total | 125 hours (5 ECTS) |
Assessment Methods / Grading
The evaluation of students is based on laboratory exercises (20% of grading) and written exam (80% of grading). Students are informed about the grading criteria from the beginning of the course.
Teaching Language
Greek (English for Erasmus students)