Master of Science in Smart Systems Engineering

Artificial Neural Networks(ISI 522)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
ISI 522 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.
Print the course contents
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 15
Presentation 0 0
Midterm Examinations (including preparation) 1 25
Project 1 20
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
1
2 X
3
4
5 X
6
7 X
8
9
10 X
11
12
Activities Number Period Total Workload
Class Hours 14 3 42
Working Hours out of Class 14 2 28
Assignments 4 5 20
Midterm Examinations (including preparation) 1 20 20
Project 1 20 20
Final Examinations (including preparation) 1 20 20
Total Workload 150
Total Workload / 25 6.00
Credits ECTS 6
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