Knowledge Engineering (CMPE465) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Knowledge Engineering	CMPE465	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

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 is designed to provide the skills needed to develop computer programs that contain large amount of knowledge, rules and reasoning mechanisms to provide solutions to real-world problems.
Course Learning Outcomes	The students who succeeded in this course; Use different methods to represent knowledge to aid the acquisition, validation and re-use of knowledge Apply rule-based, graphical or logical techniques in knowledge representation Develop expert systems Practice with the tools require to develop ontologies and semantic webs Describe the basics of machine learning
Course Content	Knowledge representation methods: rule-based, graph-based, logic-based methods, introduction to Prolog, knowledge acquisition, expert systems, ontology, semantic web, introduction to machine learning.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction	Chapter 1 (main text)
2	Knowledge Representation	Chapter 1
3	Rule-Based Knowledge Representation	Chapter 7
4	Graph-Based Knowledge Representation	Chapter 8
5	Semantic Nets	Lecture Notes
6	Frames	Chapter 8
7	First-Order Logic	Chapter 2
8	Introduction to Prolog - I	(Other sources 3)
9	Introduction to Prolog - II	(Other sources 3)
10	Knowledge Acquisition	(Other sources 2)
11	Expert Systems	(Other sources 2)
12	Semantic Web	(Other sources 4)
13	Ontology	(Other sources 4)
14	Machine Learning	Lecture Notes

Sources

Course Book	1. Knowledge Representation and Reasoning, R.J.Brachman and H.J.Levesque, Morgan Kaufmann, 2004.
Other Sources	2. 1. Knowledge Representation: Logical, Philosophical, and Computational Foundations, John F. Sowa, Brooks/Cole, Thomson Learning, 2000.
	3. 2. Introduction to Expert Systems, Peter Jackson, Addison-Wesley, 1999,
	4. 3. Programming in Prolog, W.F.Cloksin, C.S. Mellish, Springer-Verlag, 1981.
	5. 4. W3C Semantic Web Activity, www.w3.org
	6. 5. Reasoning about Knowledge, R. Fagin, J.Y.Halpern, Y. Moses, and M.Y.Vardi, MIT Press, 2003.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	-	-
Presentation	-	-
Project	1	30
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	30
Final Exam/Final Jury	1	40
Toplam	3	100

Percentage of Semester Work	60
Percentage of Final Work	40
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 computing fields; ability to apply theoretical and practical knowledge of these fields in solving engineering problems related to information systems.			X
2	Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.
3	Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in information systems engineering applications; ability to use information technologies effectively.
5	Ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the information systems discipline.
6	Ability to work effectively in inter/inner disciplinary teams; ability to work individually.
7	a. Effective oral and written communication skills in Turkish; 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. b. Knowledge of at least one foreign language; 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	Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development.
9	a. Ability to behave according to ethical principles, awareness of professional and ethical responsibility. b. Knowledge of the standards utilized in information systems engineering applications.
10	a. Knowledge on business practices such as project management, risk management and change management. b. Awareness about entrepreneurship, and innovation. c. Knowledge on sustainable development.
11	a. Knowledge of the effects of information systems engineering applications on the universal and social dimensions of health, environment, and safety. b. Awareness of the legal consequences of engineering solutions.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project	1	15	15
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	15	15
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			125