| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Have the ability to understand scientific and mathematical principles and apply them to the practice of engineering.
- Have the ability to use computer programs as tools for analysis applications
- Have the ability to apply the advanced principles of measurement, data analysis, and design of experiments
- Have the ability to apply the system/process design levels to industrial engineering problems, including the consideration of different technical alternatives while bearing in mind cost, environmental concerns, safety, and other constraints.
- Have the ability to analyze, measure, test, and evaluate an industrial engineering problem
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Knowledge representation. Rule-base systems. Knowledge acquisition, decision trees, ID3 algorithm. The inference engine: forward chaining, backward chaining, and backward chaining algorithms. Inexact reasoning, uncertainty models in expert systems. Validity of knowledge base. Fuzzy expert systems. Frame-based expert systems. Hybrid systems. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Expert systems: understanding. Basic concept and structure of ES. |
|
2nd Week |
Knowledge representaton. Representation via rule-based systems. |
|
3rd Week |
Knowledge representation. Implementation rules. |
|
4th Week |
Knowledge acquisition. Examples of knowledge acquisition. |
|
5th Week |
The inference engine. The forward chaining algorithm. |
|
6th Week |
The inference engine. The backward chaining algorithm. |
|
7th Week |
Inexact reasoning. Uncertainty management in expert systems. |
|
8th Week |
Validity of knowledge base. Examples of checking. |
|
9th Week |
Midterm |
|
10th Week |
Hybrid expert systems. Practical considerations. |
|
11th Week |
Fuzzy expert systems. Fuzzy rules and inference. |
|
12th Week |
Frame-based expert systems. Designing issues. |
|
13th Week |
Knowledge engineering. Problem assessment,Design and testing. |
|
14th Week |
Knowledge-based system projects. |
|
| Recommended or Required Reading | James P. Ignizio. Introduction to Expert Systems. The Development and Implementation of Rule-Based Expert Systems, McGraw-Hill, Inc., 1991;
Michael Negnevitsky. A Guide to Intelligent Systems, Addison-Wesley, 2001
|
|---|
| Planned Learning Activities and Teaching Methods |
- Questions/Answers
- Lecture
- Practice
- Presentation
- Problem Solving
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Total(%) |
|
0 |
| Contribution of In-term Studies to Overall Grade(%) |
|
0 |
| Contribution of Final Examination to Overall Grade(%) |
|
100 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
|
|
|
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
5 |
70 |
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
|
|
|
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 144 |
| Total Work Load / 30 (h) | 4.8 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
