INTERNATIONAL BURCH UNIVERSITY
Graduate Study - Faculty of Engineering and Natural Sciences
3+2 Electrical and Electronic Engineering
2016-2017
| SYLLABUS |
| Code |
Name |
Level |
Year |
Semester |
| EEE 544 |
Computational Intelligence |
Graduate |
1 |
Fall |
| Status |
Number of ECTS Credits |
Class Hours Per Week |
Total Hours Per Semester |
Language |
| Area Elective |
6 |
3 |
150 |
English |
| This course begins with an introductions to soft computing, from fuzzy logic to genetic algorithms. After a thorough study of fuzzy logic systems, concept of machine learning in general is introduced. Major classes of machine learning are covered and implemented in various scenarios. |
| COURSE OBJECTIVE |
| This course should provide the student ability to observe data mining and machine learning methods in terms of embedded systems framework and to implement machine learning algorithms in various platforms. |
| COURSE CONTENT |
- Introduction to computational intelligence
- Fuzzy Logic Systems
- Artificial Neural Networks
- Evolutionary computation: Genetic Algorithms
- Particle Swarm Optimization
- Feature Reduction: PCA, ICA
- Midterm Review
- MIDTERM EXAM
- Post-midterm Review
- Nearest Neighbor Algorithm
- Support Vector Machines
- Unsupervised learning: Clustering
- Microsoft Azure
- Deep Learning: Google TensorFlow
- Final Exam Review
|
| Description |
- Interactive Lectures
- Excersises
- Discussions and group work
|
| Description (%) |
| Midterm Exam(s) | 1 | 20 | | Lab/Practical Exam(s) | 1 | 30 | | Attendance | 1 | 10 | | Final Exam | 1 | 40 |
|
| Learning outcomes |
- Gain a working knowledge of knowledge-based systems, neural networks, fuzzy
- Apply intelligent systems technologies in a variety of engineering applications
- Implement typical computational intelligence algorithms in MATLAB, WEKA, TensorlFlow, or Azure
- Present ideas and findings effectively
- Think critically and learn independently
|
| TEXTBOOK(S) |
- S. Theodoridis, K. Koutroumbas, An Introduction to Pattern Recognition: A MATLAB Approach, 2010.
|
| ECTS (Allocated based on student) WORKLOAD |
| Lecture (14 weeks x Lecture hours per week) | 14 | 3 | 42 | | Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) | | | 0 | | Midterm Examination (1 week) | 1 | 2 | 2 | | Final Examination(1 week) | 1 | 2 | 2 | | Preparation for Midterm Examination | 1 | 35 | 35 | | Preparation for Final Examination | 1 | 35 | 35 | | Assignment / Homework/ Project | 1 | 17 | 17 | | Seminar / Presentation | 1 | 17 | 17 |
|