CMP682 - ARTIFICIAL INTELLIGENCE

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
ARTIFICIAL INTELLIGENCE CMP682 Spring 3 0 3 9
PrequisitesNone
Course languageEnglish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Preparing and/or Presenting Reports
Experiment
Problem Solving
Project Design/Management
 
Instructor (s)Faculty members and lecturer 
Course objectiveGaining in-depth understanding on the concepts of artificial intelligence in general.  
Learning outcomes
  1. In completition of this course the student will,
  2. 1- Have a general understanding about the concepts of artificial intelligence.
  3. 2- Conduct research on AI problems.
  4. 3- Develop methods for solving open research problems on AI.
  5. 4- Conduct in-depth research and experiments on AI topics.
  6. 5- Prepare technical presentations and reports.
Course ContentProblem Solving and Search, Heuristic search algorithms, Game-playing, First-order predicate logic, Propositional Logic, Machine learning, Reinforcement Learning, Semantic nets, Perception, Applications and open research problems 
ReferencesRussell S. ve Norvig P., Artificial Intelligence: A Modern Approach (AIMA), Prentice-Hall, 2009. 

Course outline weekly

WeeksTopics
Week 1Introduction to AI
Week 2Problem Solving and Search
Week 3Heuristic search algorithms
Week 4Game-playing
Week 5First-order predicate logic
Week 6Propositional Logic
Week 7Propositional Logic
Week 8Machine learning
Week 9Reinforcement Learning
Week 10Semantic nets
Week 11Perception
Week 12Applications and open research problems
Week 13Applications and open research problems
Week 14Project Presentations
Week 15Final exam preparation
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance05
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments210
Presentation00
Project125
Seminar110
Midterms00
Final exam150
Total100
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation11818
Project16060
Homework assignment22040
Midterms (Study duration)000
Final Exam (Study duration) 12020
Total Workload33127264

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge.  X  
2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency.   X  
3. Graduates of a computer science program need to understand how theory and practice influence each other.  X  
4. Graduates need to think at multiple levels of detail and abstraction.  X   
5. Students will be able to think critically, creatively and identify problems in their research.    X
6. Graduates should have been involved in at least one substantial project.     X
7. Graduates should realize that the computing field advances at a rapid pace.    X 
8. Graduates should conduct research in an ethical and responsible manner.  X   
9. Graduates should have good command of technical terms in both Turkish and English.   X 
10. Graduates should understand the full range of opportunities available in computing.   X 
11. Graduates should understand that computing interacts with many different domains.    X 
12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems.     X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest