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	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	X
Major Area Courses
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	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems.
2	Ability to formulate, and solve complex mechatronics engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
3	Ability to design a complex mechatronics engineering system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
4	Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in mechatronics engineering and robot technology practices; ability to employ information technologies effectively.
5	Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex mechatronics engineering and robot technology problems or research questions.
6	Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7	Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8	Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9	a-) Knowledge on behavior according to ethical principles, professional and ethical responsibility b-) Knowledge on standards used in engineering practices.
10	a-) Knowledge about business life practices such as project management, risk management, and change management b-) Awareness in entrepreneurship, innovation; knowledge about sustainable development.
11	Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
12	Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions in the field of mechatronics engineering.
13	Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planning, improving or changing the norms with a criticism.

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