COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5032 ARTIFICIAL INTELLIGENCE ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM VARLIKLAR

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
GEOGRAPHICAL INFORMATION SYSTEMS (ENGLISH)
Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Biomedical Tehnologies (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

The objective of this course is to give the theoretical background to the graduate students who conduct research in fields which use artificial intelligence, such as decision making, machine learning, computer vision, bioinformatics, and robotics.

Learning Outcomes of the Course Unit

1   Define basic artificial intelligence concepts
2   Apply artificial intelligence techniques
3   Determine which Artificial Intelligence technique is appropriate to solve a particular problem
4   Design an artificial intelligent model
5   Implement an AI algorithm

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
2 Real World AI Applications
3 Pattern Discovery with AI techniques
4 Problem Solving Techniques
5 Association Rules and Decision Making
6 Artificial Neural Networks
7 Self-Organizing Map (SOM)
8 Pattern Recognition
9 Knowledge Representation
10 Artificial Life
11 Expert Systems
12 AI Robotics
13 Presentations
14 Presentations

Recomended or Required Reading

Textbook(s): M. Tim Jones, Artificial Intelligence: A Systems Approach, Jones and Bartlett Publishers, 2008, ISBN: 978-0763773373.

Planned Learning Activities and Teaching Methods

Lectures,
Research,
Application Development,
Presentation,
Term project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PAR PARTICIPATION
3 ASG ASSIGNMENT
4 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.40 + PAR * 0.10 + ASG * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic and project / report prepared by the student.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Asst.Prof.Dr. Özlem AKTAŞ
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 26
e-mail: ozlem@cs.deu.edu.tr

Office Hours

Thursday 10:00 - 11:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Preparing assignments 1 50 50
Preparing presentations 2 20 40
Reading 1 26 26
Project Final Presentation 1 2 2
TOTAL WORKLOAD (hours) 188

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.1444
LO.2222121
LO.325441
LO.421
LO.51422

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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