Artificial Intelligence
Information Systems Engineering
Faculty of Technology
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Artificial Intelligence | TBL424 | Elective | Bachelor's degree | 4 | Fall | 5 |
Name of Lecturer(s)
Prof. Dr. Mehmet YILDIRIM
Associate Prof. Dr. Süleyman EKEN
Learning Outcomes of the Course Unit
1) Explains the theory of the basic concepts of artificial intelligence
2) Uses data in accordance with the artificial intelligence model
3) Designs artificial intelligence projects
4) Classifies artificial intelligence systems
5) Explains the statistical infrastructure of artificial intelligence
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | High | High | High | Middle | Low | Middle | High | High | Middle | High | Middle | |
| 2 | Low | High | High | Middle | Low | High | High | Middle | Low | Middle | High | |
| 3 | Middle | Middle | High | Middle | High | High | Middle | Middle | Middle | Low | Low | |
| 4 | Middle | Middle | Middle | Middle | High | High | Low | Middle | Middle | Middle | Low | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Artificial intelligence concepts., Uninformed and informed search; blind search we heurus search information and inquiries; learning theory, types of learning, neural networks, information expression diagrams, semantic circuit, uncertainty, contingency planning, Markov decision process, natural language processing, image, low level vision and classification, advanced artificial intelligence applications; learning, image detection, interrogation incertainty.
Weekly Schedule
1) Course orientation, Artificial Intelligence Overview2) Goal trees, problem modelling and solving
3) Blind search
4) Heuristic search
5) Games and adversarial search
6) Propositional logic
7) Logical Programming
8) Midterm exam
9) Rule-based expert systems
10) Linner Algebra, Statistics, Probability
11) Machine learning - Regression, Classification, Clustering
12) Machine Learning with Python
13) Genetic algorithms
14) Computer vision
15) Deep learning
16) Final exam
Recommended or Required Reading
1- Artificial Intelligence, Patrick Henry Winston, 1992/Third Edition, Addison-Wesley
2- Artificial Intelligence: Foundations of Computational Agents, David L. Poole and Alan K. Mackworth, 2017/Second Edition, Cambridge University Press
3- The Quest for Artificial Intelligence: A History of Ideas and Achievements, Nils Nilsson, 2009, Cambridge University Press
4- Python for Data Analysis, 2nd Edition Data Wrangling with Pandas, NumPy, and IPython, William McKinney, 2017
5- Learning scikit-learn: Machine Learning in Python– November 25, 2013, Raúl Garreta, Guillermo Moncecchi
6- Building Machine Learning Systems with Python, Willi Richert, Luis Pedro Coelho, 2013
7- Prolog Programming for Artificial Intelligence, Ivan Bratko, 2011, Pearson Education
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Drill and Practice
4) Group Study
5) Lab / Workshop
6) Project Based Learning
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
60% |
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Contribution of Final Examination to Course Grade |
40% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required