Applied Neural Computing (CMPE461) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Applied Neural Computing	CMPE461	Area Elective	2	2	0	3	5

Pre-requisite Course(s)
MATH275

Course Language	English
Course Type	Technical Elective Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	This course has the objective to provide an introduction to neural network architectures, learning algorithms, and their applications.
Course Learning Outcomes	The students who succeeded in this course; Describe the concepts and techniques of neural networks Reason about the behavior of neural networks Evaluate which neural network model is appropriate to a particular application Evaluate pros and cons of neural network models Apply neural networks to particular applications Identify steps to take to improve performance of the algorithms
Course Content	Introduction to neural networks, perceptron learning rules, backpropagation algorithms, generalization and overtraining, adaptive linear filters, radial basis networks, self organizing networks, learning vector quantization, recurrent networks.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction to neural networks.	Chapter 1 (main text)
2	Perceptron learning rules	Chapter 5.1-5.3
3	Linear, nonlinear, and stochastic units in simple perceptrons and applications	Chapter 5.4-5.7
4	Backpropagation	Chapter 6.1
5	Variations on backpropagation and applications	Chapter 6.2, 6.3
6	Generalization and overtraining	Chapter 6.4-6.6
7	Recurrent networks	Chapter 7
8	Unsupervised learning	Chapter 8.1-8.3
9	Self organizing networks	Chapter 8.4
10	Adaptive linear filters	Chapter 9.1-9.4
11	Learning vector quantization	Chapter 6.3 (Other sources 2)
12	Radial basis networks	Chapter 5 (Other sources 1)
13	Applications of neural networks	Various sources
14	Applications of neural networks	Various sources

Sources

Course Book	1. Hertz, Krogh, & Palmer (1991) Introduction to the Theory of Neural Computation. Addison-Wesley.
Other Sources	2. 1. Bishop (2005). Neural Networks for Pattern Recognition. Oxford University Press.
	3. 2. Ripley, Ripley, & Hjort (1996). Pattern Recognition and Neural Networks. Cambridge University Press.
	4. 3. Haykin (1999). Neural Networks: A Comprehensive Foundation (2nd Edition) Macmillan.
	5. 4. Anderson, & Rosenfeld (1998) Neurocomputing: Foundations of Research, MIT Press, Cambridge.
	6. 5. Mitchell (1997). Machine Learning, McGraw Hill, New York.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	2	10
Presentation	-	-
Project	2	40
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	20
Final Exam/Final Jury	1	30
Toplam	6	100

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses
Major Area Courses
Supportive Courses	X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Adequate knowledge in mathematics, science and subjects specific to the software engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.			X
2	The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.				X
3	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose.					X
4	The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; the ability to utilize information technologies effectively.				X
5	The ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.
6	The ability to work effectively in inter/inner disciplinary teams; ability to work individually.			X
7	Effective oral and written communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8	The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
9	Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development			X
10	The ability to behave according to ethical principles, awareness of professional and ethical responsibility.
11	Knowledge of the standards utilized in software engineering applications.
12	Knowledge on business practices such as project management, risk management and change management.
13	Awareness about entrepreneurship, and innovation.
14	Knowledge on sustainable development.
15	Knowledge of the effects of software engineering applications on the universal and social dimensions of health, environment, and safety.
16	Awareness of the legal consequences of engineering solutions.
17	An ability to apply algorithmic principles, mathematical foundations, and computer science theory in the modeling and design of computer-based systems with the trade-offs involved in design choices.			X
18	The ability to apply engineering approach to the development of software systems by analyzing, designing, implementing, verifying, validating and maintaining software systems.			X

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	4	64
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	1	16
Presentation/Seminar Prepration
Project	2	10	20
Report
Homework Assignments	2	4	8
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	7	7
Prepration of Final Exams/Final Jury	1	10	10
Total Workload			125