Instructor Assoc. Prof Dr. Mehmet Bodur
Announcements
Welcome to Knowledge Engineering.
Course Description
An overview of AI, Knowledge-based systems-a survey; Knowledge Engineering concepts; Human Problem Solving; Human Information Processing System; Cognition Models; Knowledge Acquisition; Knowledge Representation; Production Rules; Inference, Forward Chaining, Backward Chaining, Mixed Chaining; Uncertainty, Certainty Factors, Bayesian, Fuzzy set based and Dempster-Shafer methods; Automated Knowledge Acquisition, Machine Learning Approaches in Expert Systems, Rule and Decision-Tree Induction; Connectionist Expert Systems; Expert System Building Tools, Development languages, Shells, Environments; Expert system design using rule-based shells; Expert system development life-cycle; Blackboard architectures; Truth Maintenance Systems.
AI ve bilgi tabanlı sistemlere genel bakış; Bilgi Mühendisliği kavramları; İnsanlarda Problem Çözme ve Bilgi İşleme Sistemleri; Algılama Modelleri; Bilgi toplama; Bilginin temsili; Üretim Kuralları; Çıkarım, İleri Zincirleme, Geriye Zincirleme, Karışık Zincirleme; Belirsizlik, Kesinlik Faktörleri, Bayes, Bulanık kümeler ve Dempster-Shafer yöntemleri; Otomatik Bilgi Toplama, Uzman Sistemlerde Makinesel Öğrenme Yaklaşımları, Kural, ve Karar-Ağacı ile İndüksiyon; Bağlantısal Uzman-Sistemler; Uzman-Sistem Oluşturma Araçları ve Geliştirme dilleri, Kabuklar, Ortamlar; Kural-tabanlı kabuklar kullanarak uzman-sistem tasarımı; Uzman-sistem geliştirme yaşam-döngüsü; Yazı tahtası mimarileri; Doğruluk-Bakım Sistemleri.
Text Books
1- Nikolai K. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, Oct 1996 ISBN 0-262-11212-4, 550 pp. 282 illus. MIT Press.
2- Simon Kendal Malcolm Creen, An Introduction to Knowledge Engineering. Springer-Verlag London Limited 2007.
3 - Hans-Jürgen Zimmermann, Fuzzy Set Theoryand Its Applications, Ed.4, Springer Science+Business Media, LLC. 1996-2001, ISBN 978-94-010-3870-6, ISBN 978-94-010-0646-0 ,
Schedule
Tuesday 14:30 and 15:30 @ CMPE036 & Wednesday 13:30 @ CMPE035
Attendance and Make-Up Exam Policy:
Minimum 70% Attendance is credited by
3 points,
85% and over is credited by
5 points.
There is
no make-up exam for Quizzes. Students missing the Mid-term or Final exam must submit a legitimate excuse within 3 working days after the exam date in order to qualify for a make-up. Only one makeup exam will be given for one of the missed exams (midterm or final) at the end of the semester that will cover all the topics of the course.
Any attempt to
cheating in
exams and
quizzes will start disciplinary action and result in fail (F).
Any attempt to
plagiarise in
homework and/or projects will set
grade of all homeworks to zero, together with disciplinary action.
Grading Policy
Any
trial of cheating will cause a
discipline reaction and a
fail in the course
Quizzes
|
20%
|
|
Attendance
|
5%
| |
Midterm examination
|
25%
|
|
Homework and
Projects
|
20%
| |
Final Exam
|
30%
| |
Course Outline
Week,
|
Topic
|
1
|
An overview of AI, Knowledge-based systems - a survey; Knowledge Engineering concepts;
|
2
|
Human Problem Solving; Human Information Processing System .
|
3
|
Major issues in KE. Typical KE problems and tools .
|
4
|
KE and Symbolic Artifical Intelligence, Data, Information, Knowledge. Data Analysis,
|
5
|
Data and Knowledge Representation. Methods for Symbolic Manipulation and Inference. Forward Chaining, Backward Chaining, Mixed Chaining; Propositional Logic and PROLOG. Production Rules, Expert Systems; Architecture and Design, Knowledge Acquisition
|
6
|
Uncertainty, Certainty Factors, Bayesian, Fuzzy set based and Dempster-Shafer methods; Fuzzy Sets and Systems: Extension principle; Fuzzy Proposition and Fuzzy Logic; Fuzzy Rules and Inference; Fuzzy Databases; Clustering-Based Methods for Fuzzy Rule Extraction, Fuzzy Prediction
|
7
|
Midterm Exam
|
8
|
Neural Networks: Biological and Artifical Neurons; Supervised Learning; Recurrent Networks;
|
9
|
Unsupervised Learning; Kohonen Maps; Neural Networks as Associative Memories
|
10
|
Automated Knowledge Acquisition, Machine Learning Approaches in Expert Systems, Rule and Decision-Tree Induction;
|
11
|
Connectionist Expert Systems;
|
12
|
Expert System Building Tools, Development languages, Shells, Environments;
|
13
|
Expert system design using rule-based shells;
|
14
|
Expert system development life-cycle; Blackboard architectures; Truth Maintenance Systems.
|
