Course Details

ELE685 - Neural Networks

2023-2024 Fall term information
The course is not open this term
ELE685 - Neural Networks
Program Theoretýcal hours Practical hours Local credit ECTS credit
MS 3 0 3 8
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Case Study, Problem Solving
Course objective : The course objective is to study on comprehension of the learning paradigms and their network realizations together with introducing well known neural network topologies and their associated learning algorithms. The course would more emphasize the signal processing aspects of neural network tools in engineering applications.
Learning outcomes : A student completing the course successfully will know some basic and pioneering efforts in learning and classification problems, Study knowledge representations and basic learning units, Have the fundamental optimisation theory and links to learning paradigms, Compare the standarts methods and Neural networks approaches in the scope of classification and learning, Learn important classes in Neural Networks in terms of supervised vs unsupervised and dynamic vs static requirements of engineering problems, Have the fundamental knowledge to follow and understand advanced up-to-date neural network algorithms. Efficiently use relevant computer programming tools for developing problem solutions.
Course content : 1. Introduction to neural networks, 2. Fundamental concepts ? neuron models, Mc Culloch Pitts model, Rosenblatt?s perceptron, learning, 3. Regression and optimization: Least square estimation, recursive least square estimation, derivative based optimization, 4. Single layer perceptrons, 5. Multilayer perceptrons, 6. Self organizing systems: Hebbian learning, Kohonen map, 7. Dynamic networks: Time delay neural networks, recurrent neural networks 8. Radial basis networks
References : Haykin, S., Neural Networks, A comprehensive Foundation, Prentice Hall, 2nd ed., 1999.; ; Jang, J.S.R., Sun T.S., Mizutani, E., Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997.; ; Lau, C., edt., Neural Networks, Theoretical Foundations and Analysis, IEEE Press, 1992.; ; Cichocki, A., Unbehauen, R. ,Neural Networks for Optimization and Signal Processing, Wiley,1993. ; ; Shalkoff, R.J., ?Artificial Neural Networks?, Mc Graw Hill, 1997.; ; Haykin. S., ? Adaptive Filter Theory?, Prentice Hall, 1996.
Course Outline Weekly
Weeks Topics
1 Introduction to neural networks
2 Fundamental concepts ? neuron models, Mc Culloch Pitts model
3 Fundamental concepts ? Knowledge representation, Rosenblatt?s perceptron, learning paradigms
4 Regression and optimization: least square estimation, recursive least square estimation,
5 Regression and optimization: derivative based optimization
6 Single layer perceptrons
7 Multilayer perceptrons: Backpropagation algorithm
8 Multilayer perceptrons: Programming considerations, applications
9 Midterm Exam
10 Self orginizing systems: Hebbian lerning, Kohonen map
11 Dynamic networks: time delay neural networks
12 Dynamic networks: recurrent neural networks
13 Radial basis networks
14 Engineering applications and comparisons
15 Final exam
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 3 45
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 15
Final exam 1 40
Total 100
Percentage of semester activities contributing grade success 60
Percentage of final exam contributing grade success 40
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 13 4 52
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 3 25 75
Quiz 0 0 0
Midterms (Study duration) 1 15 15
Final Exam (Study duration) 1 25 25
Total workload 32 72 209
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.
4. Designs and runs research projects, analyzes and interprets the results.
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.
6. Produces novel solutions for problems.
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.
8. Follows technological developments, improves him/herself , easily adapts to new conditions.
9. Is aware of ethical, social and environmental impacts of his/her work.
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
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