ACADEMICS
Course Detail

ELE 685 Neural Networks
2017-2018 Fall term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://ects.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.

ELE685 - NEURAL NETWORKS

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
NEURAL NETWORKS ELE685 Any Semester/Year 3 0 3 8
Prerequisite(s)
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Case Study
Problem Solving
 
Instructor (s)Assoc.Prof. Atila Yżlmaz 
Course objectiveThe 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
  1. A student completing the course successfully will know some basic and pioneering efforts in learning and classification problems,
  2. Study knowledge representations and basic learning units,
  3. Have the fundamental optimisation theory and links to learning paradigms,
  4. Compare the standarts methods and Neural networks approaches in the scope of classification and learning,
  5. Learn important classes in Neural Networks in terms of supervised vs unsupervised and dynamic vs static requirements of engineering problems,
  6. Have the fundamental knowledge to follow and understand advanced up-to-date neural network algorithms.
  7. Efficiently use relevant computer programming tools for developing problem solutions.
Course Content1. 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
 
ReferencesHaykin, 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

WeeksTopics
Week 1Introduction to neural networks
Week 2Fundamental concepts ? neuron models, Mc Culloch Pitts model
Week 3Fundamental concepts ? Knowledge representation, Rosenblatt?s perceptron, learning paradigms
Week 4Regression and optimization: least square estimation, recursive least square estimation,
Week 5Regression and optimization: derivative based optimization
Week 6Single layer perceptrons
Week 7Multilayer perceptrons: Backpropagation algorithm
Week 8Multilayer perceptrons: Programming considerations, applications
Week 9Midterm Exam
Week 10Self orginizing systems: Hebbian lerning, Kohonen map
Week 11Dynamic networks: time delay neural networks
Week 12Dynamic networks: recurrent neural networks
Week 13Radial basis networks
Week 14Engineering applications and comparisons
Week 15Final exam
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments345
Presentation00
Project00
Seminar00
Midterms115
Final exam140
Total100
Percentage of semester activities contributing grade succes460
Percentage of final exam contributing grade succes140
Total100

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)13452
Presentation / Seminar Preparation000
Project000
Homework assignment32575
Midterms (Study duration)11515
Final Exam (Study duration) 12525
Total Workload3272209

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
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.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.    X
4. Designs and runs research projects, analyzes and interprets the results.   X 
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.   X 
6. Produces novel solutions for problems.   X 
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.   X 
8. Follows technological developments, improves him/herself , easily adapts to new conditions.    X 
9. Is aware of ethical, social and environmental impacts of his/her work.  X  
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively  X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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