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| IST4019 | Introduction to Artificial Intelligence | 4+0+0 | ECTS:6 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Prof. Dr. Orhan KESEMEN | | Co-Lecturer | DOCTOR LECTURER Tolga BERBER | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To teach various approaches for problem solving, the basic knowledge in machine learning, and research various approaches in computer vision, natural language processing and to make the students apply them . The Topics covered include search (solving puzzles, playing games) , planning, logical inference (drawing conclusions from data) , expert systems, and machine learning. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | acquire knowledge of various approaches of problem solving and apply them | 1,3,4,9 | 1,3 | | LO - 2 : | acquire knowledge of various approaches of base information about machine learning | 1,3,4,9 | 1,3 | | LO - 3 : | research various approaches to computer vision and natural language processing.and apply them | 1,3,4,9 | 1,3 | | LO - 4 : | create programs using heuristics for problem solving | 1,3,4,9 | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Introduction to Artificial Intelligence: Learning the basic AI techniques; the problems for which they are applicable; their limitations. State Space Search: Defining the problems space, operators, state space search, goal state; Blind Search: Learning about a basic search strategy; Heuristic Search: Learning about heuristic evaluation functions; Learn about hill climbing techniques; Best First Search: Learn about best first and A* search; Compare various search algorithms; Heuristic functions; Minimax Search: Learning about two player games; Learning about game evaluation functions; Learning about minimax search; Learning about depth bounded search; Learning about alpha beta; an admissible search heuristic for minimax; Learning to identify best move from game tree and nodes proned by alpha beta cutoffs; Expert Systems: Learning about Expert systemsNatural Language Processing: Learning about- Problems in natural language processing; Grammars; Parsing; Defining clause Grammars; Building Parse Trees; Machine Learning: Learn about the Goals of Learning Programs; Evaluating Learning Programs; Learning Conjunctive Rules; Classifying with Decision Trees; Learning Decision Trees; |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Artificial Intelligence: Learn the basic AI techniques; applicable techniques and the examination of their limits | | | Week 2 | State Space Search: Defining the problems space,operators,state space search,its objective and condition | | | Week 3 | Blind Search: Learning about basic search strategies; | | | Week 4 | Heuristic Searches: Learning about heuristic evaluation functions; | | | Week 5 | Learning about top going techniques; | | | Week 6 | Best First Search: Learining about best first and A* searches; | | | Week 7 | Comparing various search algorithms; | | | Week 8 | Heuristic functions; Minimax Searches: Learning about two player games; | | | Week 9 | Mid-term exam | | | Week 10 | Learning about game evaluation functions; Learning about minimax searches; | | | Week 11 | Learning about depth limits; Learning about alpha beta; an acceptable heuristic search for minimax; | | | Week 12 | Expert Systems: Learn about Expert systems | | | Week 13 | Natural Language Processing:Problems in natural language processing; | | | Week 14 | Grammar, Parsing, Defining grammatical sentences,Building a Parse Tree | | | Week 15 | Computer Learning: Goals of Learning Programs; Evaluating Learning Programs; | | | Week 16 | End-of-term exam | | | |
| 1 | Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, Prentice-Hall (2003 - 2nd Edition) | | | |
| 1 | Vasif V. NABİYEV, 2003, Yapay Zeka, problemler ? yöntemler ? algoritmalar, Seçkin Yayınevi, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 25/11/2021 | 2 | 50 | | End-of-term exam | 16 | 11/01/2022 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Ödev | 3 | 10 | 30 | | Dönem sonu sınavı için hazırlık | 6 | 1 | 6 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 135 |
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