| INDUSTRIAL PRODUCTS DESIGN | |||||
| 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. |
|
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) | Having the theoretical and practical knowledge proficiency in the discipline of industrial product design | |
| 2) | Applying professional knowledge to the fields of product, service and experience design development | |
| 3) | Understanding, using, interpreting and evaluating the design concepts, knowledge and language | |
| 4) | Knowing the research methods in the discipline of industrial product design, collecting information with these methods, interpreting and applying the collected knowledge | |
| 5) | Identifying the problems of industrial product design, evaluating the conditions and requirements of problems, producing proposals of solutions to them | |
| 6) | Developing the solutions with the consideration of social, cultural, environmental, economic and humanistic values; being sensitive to personal differences and ability levels | |
| 7) | Having the ability of communicating the knowledge about design concepts and solutions through written, oral and visual methods | |
| 8) | To identify and apply the relation among material, form giving, detailing, maintenance and manufacturing methods of design solutions | |
| 9) | Using the computer aided information and communication technologies for the expression of industrial product design solutions and applications | |
| 10) | Having the knowledge and methods in disciplines like management, engineering, psychology, ergonomics, visual communication which support the solutions of industrial product design; having the ability of searching, acquiring and using the knowledge that belong these disciplines when necessary. | |
| 11) | Using a foreign language to command the jargon of industrial product design and communicate with the colleagues from different cultures | |
| 12) | Following and evaluating the new topics and trends that industrial product design needs to integrate according to technological and scientific developments |