BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| PILOTAGE (EN) | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP4501 | Introduction to Artificial Intelligence and Expert Systems | Fall | 3 | 0 | 3 | 6 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| 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. |
Learning Outcomes
|
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. |
Course Content
| 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. |
Weekly Detailed Course Contents
| 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. |
Sources
| 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 |
Evaluation System
| 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 | |
ECTS / Workload Table
| 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 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Apply scientific methods or concepts in solving aviation-related problems | 3 |
| 2) | Analyze and interpret data | 5 |
| 3) | Work effectively on multi‐disciplinary and diverse teams | 5 |
| 4) | Make professional and ethical decisions | 3 |
| 5) | Communicate effectively, using both written and oral communication skills | 4 |
| 6) | Engage in and recognize the need for life‐long learning | 5 |
| 7) | Use the techniques, skills, and modern technology necessary for professional practice | 4 |
| 8) | Assess the national and international aviation environment | 3 |
| 9) | Apply pertinent knowledge in identifying and solving contemporary problems in the field of aviation | 3 |
| 10) | Apply knowledge of business sustainability to aviation issues | 3 |