Artificial Neural Networks(IND 561)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| IND 561 | Artificial Neural Networks | 2 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Sadettin Emre ALPTEKİN ealptekin@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The aim of this course is to introduce artificial neural networks and discuss the basic ideas behind machine learning; present the concept of perceptron as a simple computing element and consider the perceptron learning rule; to introduce recurrent neural networks; explore Hebbian and competitive learning. Moreover, hybrid intelligent systems as a combination of different intelligent technologies will be introduced and evolutionary neural networks and fuzzy evolutionary systems will be discussed. |
| Content |
1. week : Introduction to knowledge-base intelligent systems 2. week : Rule-based expert systems 3. week : Uncertainty management in rule-based expert systems 4. week : Fuzzy expert systems: Fuzzy logic 5. week : Frame-based expert systems 6. week : Artificial neural networks: Supervised learning 7. week : Artificial neural networks: Unsupervised learning 8. week : Evolutionary Computation: Genetic algorithms 9. week : Mid term 10. week : Evolutionary Computation: Evolution strategies and genetic programming 11. week : Hybrid intelligent systems: Neural expert systems and neuro-fuzzy systems 12. week : Hybrid intelligent systems: Evolutionary neural networks and fuzzy evolutionary systems 13. week : Knowledge engineering: Building neural network based systems 14. week : Data mining and knowledge discovery |
| Course Learning Outcomes |
Upon successful completion of this course, students should be able to: 1. Define the principles behind intelligent systems. 2. Recognize what intelligent systems can and cannot do. 3. Enumerate typical applications of intelligent systems. 4. Explain which tools are most relevant for the task on-hand. 5. Explain how to use different artificial intelligence tools. 6. Explain how to build an intelligence system. 7. Design an artificial neural network. |
| Teaching and Learning Methods | Use of slideshow during the class and available online. Exercises applications during each class. Students propose seminar with reports and presentation. |
| References | Negnevitsky, M., Artificial Intelligence: A Guide to Intelligent Systems, Second Edition, Addison Wesley, 2004. |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction, Artificial Intelligence, Machine Learning |
| 2 | Linear Algebra Review |
| 3 | Linear regression with one variable and with multiple variables |
| 4 | Logistic regression with one variable and with multiple variables |
| 5 | Regularization |
| 6 | Neuron models and basic learning rules |
| 7 | Multi-layer perceptron |
| 8 | Midterm Examination |
| 9 | Different architectures |
| 10 | Associative memory and Hopfield Neural Network |
| 11 | Distance Based Neural Networks I |
| 12 | Distance Based Neural Networks II |
| 13 | Neural Network Trees |
| 14 | Clustering |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 4 | 10 |
| Presentation | 1 | 25 |
| Midterm Examinations (including preparation) | 1 | 25 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 6 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
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| 12 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 16 | 6 | 96 |
| Presentation | 14 | 3 | 42 |
| Midterm Examinations (including preparation) | 1 | 2 | 2 |
| Final Examinations (including preparation) | 1 | 2 | 2 |
| Reports | 14 | 1 | 14 |
| Total Workload | 198 | ||
| Total Workload / 25 | 7,92 | ||
| Credits ECTS | 8 | ||