| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP4501 | Introduction to Artificial Intelligence and Expert Systems | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi TEVFİK AYTEKİN |
| 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. |
|
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: | 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 |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 2 | % 10 |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentations / Seminar | |||
| Project | 4 | 20 | |
| Homework Assignments | 10 | 20 | |
| Quizzes | 2 | 8 | |
| Preliminary Jury | |||
| Midterms | 5 | 15 | |
| Paper Submission | |||
| Jury | |||
| Final | 5 | 20 | |
| Total Workload | 125 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |