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 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 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
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
1 2 3 4 5
1 Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose. X
3 Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. X
4 Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively. X
5 Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline. X
6 Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering. X
7 Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions.
8 Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.
9 Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself. X
10 Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities.
11 Knows the standards used in software engineering practices.
12 Knows about business practices such as project management, risk management and change management.
13 Gains awareness about entrepreneurship and innovation.
14 Gains knowledge on sustainable development.
15 Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering.
16 Acquires awareness of the legal consequences of engineering solutions.
17 Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience. X
18 Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes. X

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