Partial Knowledge and Heuristic Function Design; Evaluating induced AI level ( Confusion Matrix ); Production System in AI, Design of Heuristic Function with examples; Heuristic Search Techniques (alpha beta pruning; A*), Problem-Solving As Search, Applied evaluation of Power of Heuristic Function ( penetration factor and effective branching factor), First Order Logic, Reasoning In First-Order Logic, Logical Agents. Inference Engines, Learning; Learning types for human and machine; Machine Learning Classification and Methods (Supervised / unsupervised, ANN, SVM, Genetic Programming), Predicate Logic; introduction to Natural Language Understanding, introduction to World Knowledge.
Prerequisite(s)
CSE0428
Corequisite(s)
N/A
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
1- George F. Luger ; Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th Edition) ; Addison Wesley; 6 edition ; (March 7, 2008); 784 pages; ISBN-13: 978-0321545893
2- Stuart Russell & Peter Norvig ; Artificial ıntelligence : A modern approach ; Prentice hall, second edition 2003; ISBN 0-13-080302-2
3- Ronald Brachman and Hector Levesque; Knowledge Representation and Reasoning (The Morgan Kaufmann Series in Artificial Intelligence) ; Morgan Kaufmann; 2004; 381 pages, ISBN-13: 978-1558609327
4- Toshinori Munakata ; Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science) ; Springer; 2nd edition, 2008 ; 260 pages ; ISBN-13: 978- 1846288388
5- George F. Luger and William A Stubblefield ; AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java ; Addison Wesley;6 edition, 2008; ISBN-13: 978-0136070474
Other Sources
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Course Schedules
Week
Contents
Learning Methods
1. Week
evaluating the level of artificial intelligence by confusion matrix
2. Week
Partial Knowledge and Heuristic Function Design; examples will be given with 8 queen and 8 puzzle. Duality of the search and heuristic function
3. Week
Production System in AI, Design of Heuristic Function with examples; State space of the complex problems, production systems, partial knowledge and search techniques
4. Week
Internal structure of game playing programs, A* algorithm, examples will be given by 8 puzzle and backgammon
5. Week
Applied evaluation of Power of Heuristic Function ( penetration factor and effective branching factor), Production rule system, learning, artificial neural networks
6. Week
Agents that reason logically, first order predicate logic
7. Week
Agents that reason logically, first order predicate logic
8. Week
Database, knowledge base, feedback level and related topics
9. Week
Evaluating power of the heuristic function, logical reasoning systems
10. Week
Planning & acting
11. Week
Probabilistic reasoning systems, introduction to fuzzy logic
12. Week
Feature extraction must give way to strategical subspacing, learning types, and the effect of strategical subspacing on learning systems
13. Week
Artificial Neural Networks (ANN), basic ANN types
14. Week
Review Final Projects
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
27
Attendance
1
6
Laboratory
1
27
Final Exam
1
40
Program Outcomes
PO-1
an ability to apply knowledge from undergraduate and graduate engineering and other disciplines to identify, formulate, and solve novel and complex electrical/computer engineering problems that require advanced knowledge within the field
PO-2
knowledge of advanced topics within at least two subdisciplines of computer engineering
PO-3
the ability to understand and integrate new knowledge within the field;
PO-4
the ability to apply advanced technical knowledge in multiple contexts
PO-5
a recognition of the need for, and an ability to engage in, life-long learning
PO-6
the ability to plan and conduct an organized and systematic study on a significant topic within the field
PO-7
an ability to convey technical material through formal written reports which satisfy accepted standards for writing style
PO-8
the ability to analyze and use existing literature
PO-9
the ability to demonstrate effective oral communication skills
PO-10
the ability to stay abreast of advancements in the area of computer engineering
Learning Outcomes
LO-1
Understanding limits, achievements and basic techniques of Artificial Intelligence.
LO-2
A working knowledge with production systems that may be applied to puzzles and two person-zero sum games is achieved.
LO-3
Basic understanding of learning, heuristic function design and intelligent agent is achieved.
