Students will have an overview of the different approaches to AI and different AI methods. They will understand the drawbacks and the advantages of different approaches and will be able to recognize the types of problems that can be tackled successfully using AI methods. Students will gain practical programming experience in solving diverse AI problems, including state space search, game playing, automatic inference, logic programming, neural networks, and biologically inspired optimization.
- define the basic concepts of artificial intelligence
- distinguish between symbolic and connectivistic approaches to AI
- apply state search algorithms and biologically inspired optimization algorithms on basic problems
- solve basic problems using logic programming
- apply inference algorithms on basic logical problems
- compare among various approaches to representing uncertainty
- assess the applicability of different AI methods on a given AI problem
- review the philosophical aspects of artificial intelligence
Forms of Teaching
Lectures are given for 13 weeks (3 hours each). Lectures are divided in two study periods. There will be a midterm exam after the first period and a final exam after the second period.Exams
Midterm exam and final exam; short quizzes during the semester.Laboratory Work
There will be 4-5 lab take-home lab assignments, demonstrated to the instructor or the lab assistent.Consultations
Weekly office hours.
|Type||Threshold||Percent of Grade||Comment:||Percent of Grade|
|Laboratory Exercises||25 %||25 %||25 %||25 %|
|Quizzes||0 %||5 %||0 %||0 %|
|Mid Term Exam: Written||0 %||35 %||0 %|
|Final Exam: Written||0 %||35 %|
|Exam: Written||50 %||37.5 %|
|Exam: Oral||37.5 %|
Exams after the final exam are written and oral, together contributing 75% to the final grade. Students are required to score above 50% on written exam to be admitted to the oral exam.
Week by Week Schedule
- Overview of the artificial intelligence. History of AI. AI research areas and newest trends. Relation to other disciplines. Intelligence and the Turing test.
- Solving problems by searching state space search. Blind search strategies.
- Informed search strategies. A* algorithm. Constraint satisfaction problems. Game playing. Min-max algorithm.
- Knowledge and reasoning. First order logic. Theorem proving. Unification. Resolution rule.
- Logic programming. Prolog.
- Semantic networks, frames and rules. Ontologies. Expert systems.
- Natural language processing.
- Midterm exam.
- Uncertain knowledge and reasoning. Probability-based knowledge representation. Bayes rule. Fuzzy logic and fuzzy inference.
- Introduction to machine learning. Naive Bayes classifer.
- Connectivistic approaches to AI. Artificial neural networks. Perceptron algorithm. Backpropagation algorithm.
- Computational intelligence. Genetic algorithm. Ant colony optimization.
- Embodied AI. Behavior-oriented AI.
- Philosophical foundations of AI. Wrap up.
- Final exam.
Software Engineering and Information Systems -> Computing (Module)
Computer Science -> Computing (Module)