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ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (MASTER) (WITH THESIS) (ENGLISH) | |
| Code | CENG568 |
| Name | Intelligent Optimization Techniques |
| Term | 2018-2019 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. UMUT ORHAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
In this course, optimization basis of artificial intelligent algorithms like artificial neural networks and support vector machine and the applications on their solutions is aimed.
Course Content
K-Means, K-NN, Decision trees ID3, C4.5, Bayesian and Naive Bayes , Least squares and linear regression, Perceptron, Adaline, Least Mean Squares, Levenberg- Marquartd and artificial neural networks, Reinforcement Learning, Q-Learning, TD-Learning, Learning Vector Quantization Network, Radial Basis Function Network, Lagrange Method and Support Vector Machine, Principal Component Analysis, Linear Discriminant Analysis, Fuzzy Logic and Fuzzy Inference System.
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows distance based methods |
| LO02 | Knows decision trees |
| LO03 | Knows regression based approach |
| LO04 | Knows and applies artificial neural network models |
| LO05 | Knows and applies mapping data |
| LO06 | Knows fuzzy inference system |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | |
| PLO02 | - | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | |
| PLO03 | - | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | |
| PLO04 | - | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | |
| PLO05 | - | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | |
| PLO06 | - | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| PLO07 | - | Has the skills of learning. | |
| PLO08 | - | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | |
| PLO09 | - | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | - | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | |
| PLO11 | - | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | |
| PLO12 | - | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Course | Reading related chapter in lecture notes | |
| 2 | Introduction to machine learning | Reading related chapter in lecture notes | |
| 3 | Distance based Clustering and Classification: K-Means and K-NN | Reading related chapter in lecture notes | |
| 4 | Entropy-based Decision Trees: ID3 and C4.5 | Reading related chapter in lecture notes | |
| 5 | Probability, Bayesian Theorem, Naive Bayes | Reading related chapter in lecture notes | |
| 6 | Least squares optimization and linear regression | Reading related chapter in lecture notes | |
| 7 | Introduction to Artificial Neural Networks: Perceptron and Adaline | Reading related chapter in lecture notes | |
| 8 | Mid-Term Exam | Study to lecture notes and applications | |
| 9 | Multi-layered artificial neural networks and Backpropagation | Reading related chapter in lecture notes | |
| 10 | Reinforcement Learning: Q and TD Learning, LVQ | Reading related chapter in lecture notes | |
| 11 | Mapping and Kernel Functions: RBF Networks | Reading related chapter in lecture notes | |
| 12 | Optimization by Lagrange Method: Support Vector Machine | Reading related chapter in lecture notes | |
| 13 | Dimension Reduction: PCA and LDA | Reading related chapter in lecture notes | |
| 14 | Fuzzy Logic and Fuzzy Inference Systems | Reading related chapter in lecture notes | |
| 15 | Project Presentations | Reading related chapter in lecture notes | |
| 16 | Term Exams | Study to lecture notes and applications | |
| 17 | Term Exams | Study to lecture notes and applications |