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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ELECTIVE

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR METE EMINAĞAOĞLU

Offered to

Computer Science

Course Objective

The main purpose of this course is to provide the most fundamental knowledge related to the AI to the students so that they can understand what the AI is.

Learning Outcomes of the Course Unit

1   Have a good understanding of the description of the AI. 2   Have a good understanding of the integration of AI with real problems. 3   Have a good understanding of the techniques used in AI 4   Have ability to make comparison between techniques used in AI and their solution ways. 5   Have a good understanding of the theory of AI techniques

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 AI: Definitions and basic terms 2 Solving problems by search algorithms 3 Heuristic search algorithms 4 Adversarial search in games 5 Constraint satisfaction problems 6 Bayesian networks -1 7 Bayesian networks -2 8 Midterm 9 Reinforcement learning -1 10 Reinforcement learning -2 11 Genetic algorithms - 1 12 Genetic algorithms - 2 13 An Introduction to Pattern Recognition and SOM 14 Project presentations

Recomended or Required Reading

Textbook(s): Russell, S.J. And Norvig, P., Artificial Intelligence : A Modern Approach, Third Edition, Prentice-Hall, 2009. Supplementary Book(s): Tom M. Mitchell, Machine Learning, McGraw Hill, 1997.

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings with numerous examples chosen to illustrate theoretical concepts.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.30 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

mete.eminagaoglu@deu.edu.tr

Office Hours

will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 12 3 36 Preparation for midterm exam 1 10 10 Preparation for final exam 1 15 15 Preparing assignment 1 30 30 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 134

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 PO.11 PO.12 PO.13 LO.1 4 5 5 3 LO.2 4 4 5 LO.3 5 LO.4 5 4 3 5 LO.5 5 5 5 4