ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NON-THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP5103 | Artificial Intelligence | Fall Spring |
3 | 0 | 3 | 8 |
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 Lecturer(s): |
Prof. Dr. NAFİZ ARICA |
Course Objectives: | The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems. |
The students who have succeeded in this course; I. Be able to solve problems by applying a suitable search method. II. Be able to implement minimax search and alpha-beta pruning in game playing. III. Be able to use logical formalisms in modeling. IV. Be able to apply supervised learning techniques to a given problem. V. Be able to apply unsupervised learning techniques to a given problem. VI. Be able to use the basic techniques in natural language processing. |
introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing. |
Week | Subject | Related Preparation | |
1) | Introduction | ||
2) | Uninformed Search Strategies | ||
3) | Uninformed Search Strategies | ||
4) | Informed (Heuristic) Search Strategies | ||
5) | Informed (Heuristic) Search Strategies | ||
6) | Adversarial Search | ||
7) | Propositional Logic | ||
8) | Predicate logic | ||
9) | Supervised Learning Techniques | ||
10) | Supervised Learning Teknileri | ||
11) | Unsupervised Learning Techniques | ||
12) | Unsupervised Learning Techniques | ||
13) | Natural Language Processing | ||
14) | Natural Language Processing | ||
15) | Final Exam |
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: |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | % 0 | |
Homework Assignments | % 0 | |
Presentation | 1 | % 10 |
Project | 1 | % 50 |
Seminar | % 0 | |
Midterms | % 0 | |
Preliminary Jury | % 0 | |
Final | 1 | % 40 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 10 | |
PERCENTAGE OF FINAL WORK | % 90 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 14 | 3 | 42 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 1 | 42 | 42 |
Homework Assignments | 7 | 8 | 56 |
Quizzes | 0 | 0 | 0 |
Preliminary Jury | 0 | ||
Midterms | 0 | 0 | 0 |
Paper Submission | 0 | ||
Jury | 0 | ||
Final | 1 | 12 | 12 |
Total Workload | 194 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Have sufficient background and an ability to apply knowledge of mathematics, science, and engineering to identify, formulate, and solve problems of electrical and electronics engineering. | |
2) | Be able to define, formulate and solve sophisticated engineering problems by choosing and applying appropriate analysis and modeling techniques and using technical symbols and drawings of electrical and electronics engineering for design, application and communication effectively. | |
3) | Have an ability to design or implement an existing design of a system, component, or process to meet desired needs within realistic constraints (realistic constraints may include economic, environmental, social, political, health and safety, manufacturability, and sustainability issues depending on the nature of the specific design). | |
4) | Elektrik ve elektronik mühendisliği yapabilmek ve yeni uygulamalara uyum gösterebilmek için gerekli yenilikçi ve güncel teknikler, beceriler, bilgi teknolojileri ve modern mühendislik araçlarını geliştirmek, seçmek, uyarlamak ve kullanmak. | |
5) | Be able to design and conduct experiments, as well as to collect, analyze, and interpret relevant data, and use this information to improve designs. | |
6) | Be able to function individually as well as to collaborate with others in multidisciplinary teams. | |
7) | Be able to communicate effectively in English and Turkish (if he/she is a Turkish citizen). | |
8) | Be able to recognize the need for, and to engage in life-long learning as well as a capacity to adapt to changes in the technological environment. | |
9) | Have a consciousness of professional and ethical responsibilities as well as workers’ health, environment and work safety. | |
10) | Have the knowledge of business practices such as project, risk, management and an awareness of entrepreneurship, innovativeness, and sustainable development. | |
11) | Have the broad knowledge necessary to understand the impact of electrical and electronics engineering solutions in a global, economic, environmental, legal, and societal context. |