| PSYCHOLOGY | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP4501 | Introduction to Artificial Intelligence and Expert Systems | Spring | 3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Non-Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi TEVFİK AYTEKİN |
| Recommended Optional Program Components: | None |
| Course Objectives: | The course introduces basics of artificial intelligence. Basic search techniques used for problem solving, fundamentals of knowledge representation and logical formalisms, basic learning algorithms, and fundamentals of expert systems will be introduced. |
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The students who have succeeded in this course; I. Be able to formulate a state space description of a problem II. Be able to select and implement brute-force or heuristic algorithm for a problem. III. Be able to implement minimax search with alpha-beta pruning. IV. Be able to compare and evaluate the most common models for knowledge representation. V. Be able to explain the operation of the resolution technique for theorem proving. VI.Be able to explain the differences among supervised and unsupervised learning. VII. Be able to explain the concepts of overfitting, underfitting, bias, and variance. VIII. Be able to implement some of the basic algorithms for supervised learning and unsupervised learning. IX. Be able to describe fundamentals of expert systems and evaluate them. |
| Introduction to AI, state spaces and searching, heuristic functions and search, alpha-beta pruning, propositional and first-order predicate logic, propositional and first order inference, unification and resolution, linear regression, logistic regression, neural networks and backpropagation algorithm, Bayes’ rule and naive Bayes algorithm, clustering and k-means algorithm, fundementals of expert systems, software for expert systems. |
| Week | Subject | Related Preparation |
| 1) | Introduction to AI | |
| 2) | State spaces and searching. | |
| 3) | Heuristic functions and search | |
| 4) | Decisions in games, alpha-beta pruning. | |
| 5) | Propositional and first-order predicate logic | |
| 6) | Propositional and first order inference | |
| 7) | Unification and resolution | |
| 8) | Linear Regression | |
| 9) | Midterm | |
| 10) | Logistic Regression | |
| 11) | Neural networks and backpropagation algorithm. | |
| 12) | Bayes’s rule and naive Bayes algorithm. | |
| 13) | Clustering and k-means algorithm | |
| 14) | Fundementals of expert systems. | |
| 15) | Software for expert systems. |
| Course Notes / Textbooks: | Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004. |
| References: | Yok - None |
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 2 | % 10 |
| Project | 1 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Project | 4 | 20 |
| Homework Assignments | 10 | 20 |
| Quizzes | 2 | 8 |
| Midterms | 5 | 15 |
| Final | 5 | 20 |
| Total Workload | 125 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Develop close interest in human mind and behavior, and attain critical thinking skills (in particular the ability to evaluate psychological theories using empirical evidence), as well as appreciating psychology as an evidence based science. | |
| 2) | Gain a biopsychosocial understanding of human behavior, namely, the biological, psychological, social determinants of behavior. | |
| 3) | Acquire theoretical and applied knowledge and learn about basic psychological concepts and perspectives | |
| 4) | Familiarize with methodology and data evaluation techniques by being aware of scientific research methods (i.e. correlational, experimental, longitudinal, case study). | |
| 5) | Employ ethical sensitivity while doing assessment, research or working with groups. | |
| 6) | Familiarize with the essential perspectives of psychology (cognitive, developmental, clinical, social, behavioral, and biological). | |
| 7) | Get the opportunity and skills to evaluate qualitative and quantitative data, write reports, and present them. | |
| 8) | Attain preliminary knowledge for psychological measurement and evaluation. | |
| 9) | To have a basic knowledge of other disciplines (e.g. sociology, history, political science, communication studies, philosophy, anthropology, literature, law, art, etc) that can contribute to psychology and to be able to make use of this knowledge in understanding and interpreting of psychological process. | 3 |