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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CME 4418 INTRODUCTION TO ARTIFICIAL INTELLIGENCE ELECTIVE 2 2 0 6

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM VARLIKLAR

Offered to

Computer Engineering

Course Objective

The main objectives of this course are to discuss, teach and apply the methods, languages, and search paradigms in AI.; increase the abilities of analytical and theoretical thinking of students, so make them able to solve the problems efficiently.

Learning Outcomes of the Course Unit

1   Learn methods and applications of artificial inteligence in daily life. 2   Learn and implement the necessary search paradigms for the solutions of mathematical problems, such as constrait satisfaction problems, when needed. 3   Use appropriate search paradigm for problem solving and produce solutions for the given problems. 4   Understand learning paradigms. 5   Apply learning paradigms in daily life and solve the problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Artificial Intelligence: History and Applications 2 Inteligent Agents 3 Search Strategies: State Space Search (Depth First, Breadth First), Heuristic Search (Best First Search, A* Method) 4 Game Trees and Adversary Search,Alpha Beta Pruning, Min Max Approach 5 Problem Solving: Constraint Satisfaction Problems (CSP), Backtracking Search for CSP 6 Learning Paradigms - I: Learning from Observations, Inductive Learning, Decision Trees 7 Learning Paradigms - II: Learning from Examples, Learning with Hidden Variables, Instance Based Learning 8 Knowledge Representation with AI Applications 9 Natural Language Processing: Syntax, Semantics and Pragmatics 10 Artificial Neural Networks 11 Genetic Algorithms 12 AI and Robotics 13 Expert Systems 14 Project Presentations

Recomended or Required Reading

Text Book: Artificial Intelligence A Modern Approach,Stuart Russell,Peter Norvig, Prentice Hall,0131038052. Supplementary Book: Artificial Intelligence, George Luger, Addison Wesley, 0201648660.

Planned Learning Activities and Teaching Methods

Presentation, Problem Solving, Homework, Quizzes

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 MTE MIDTERM EXAM 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.40 + MTE * 0.20 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.40 + MTE * 0.20 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam is 20%, Quizzes and Assignments 40%, Final exam is 40% of the course grade.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

aktas.ozlem@deu.edu.tr

Office Hours

Will be announced in the first class.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 2 28 Tutorials 14 2 28 Preparations before/after weekly lectures 14 1 14 Preparing assignments 1 18 18 Preparation for final exam 1 18 18 Preparation for quiz etc. 4 2 8 Preparation for midterm exam 1 12 12 Final 1 4 4 Quiz etc. 4 2 8 Project Assignment 1 6 6 Midterm 1 2 2 TOTAL WORKLOAD (hours) 146

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 4 5 3 LO.2 5 4 3 3 LO.3 4 3 5 3 4 LO.4 4 3 LO.5 3 3 3