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	X
Supportive Courses
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	Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems.					X
2	Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose.					X
3	Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose.				X
4	Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively.				X
5	Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering.			X
6	Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually.
7	Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.	X
8	Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions.
9	Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself.
10	Acts in accordance with ethical principles and has awareness of professional and ethical responsibility.
11	Has knowledge about the standards used in computer engineering applications.
12	Has knowledge about workplace practices such as project management, risk management, and change management.
13	Gains awareness about entrepreneurship and innovation.
14	Has knowledge about sustainable development.
15	Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering.		X
16	Gains awareness of the legal consequences of engineering solutions.
17	Analyzes, designs, and expresses numerical computation and digital representation systems.					X
18	Uses programming languages and appropriate computer engineering concepts to solve computational problems.					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