Institute of Science and Technology · Computer Science and Engineering (%30 English) · Doctorate
ECTS: 7.5 T+P+L: 3+0+0 Departmental Elective
Coordinator: Dr. Aykut GÜVEN
Instructors: Dr. Aykut GÜVEN
Course Objective
The aim of this course is to learn the artificial intelligence methods and languages and to understand the suitability of a problem for these methods.
Course Content
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems
Course Learning Outcomes
- Identifying the problems that can be solved using neural networkalgorithms and being able to solve them.
Core Area Distribution
(46) Mathematics and Statistics%20 (48) Computing%70 (52) Engineering and Engineering Trades%10
Teaching Methods
ExpressionQuestion-AnswerDiscussionExercise and PracticeExperiment - Test / Lab/ Workshop / Field PracticeProject Based Learning (Including Field Work)
Assessment & Evaluation
HomeworkProject / DesignTesting (Essay / Tests: True-Falls, multiple-choice, short answer, matching)
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 15 | 3 | 45 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 1 | 3 | 3 |
| Quiz | 0 | 0 | 0 |
| Assignment | 0 | 0 | 0 |
| Practice | 1 | 1 | 1 |
| Final | 1 | 3 | 3 |
Course Schedule
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