COURSE UNIT TITLE

: METHODS OF ARTIFICAL INTELLIGENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ETE 3005 METHODS OF ARTIFICAL INTELLIGENCE ELECTIVE 2 0 0 4

Offered By

Faculty of Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ZERRIN IŞIK

Offered to

Electrical and Electronics Engineering (English)
Industrial Engineering
Mechanical Engineering (Evening)
Mechanical Engineering
Textile Engineering
Computer Engineering (English)
Mining Engineering (Evening)
Metallurgical and Materials Engineering
Aerospace Engineering
Geological Engineering (Evening)
Environmental Engineering
Civil Engineering
Civil Engineering (Evening)

Course Objective

The main objectives of this course are to discuss, teach and apply the methods, 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
3   Understand and apply learning paradigms in daily life and solve the problems
4   Develop a project with use of an artificial inteligence approach

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 Constraint Satisfaction Problems (CSP) and Game Strategies
3 Graph and Tree Search Strategies (DFS, BFS)
4 Hill Climbing, Best First Search, A* Method
5 Introduction to Learning Techniques
6 Unsupervised Learning Methods - Kmeans Clustering
7 Unsupervised Learning Methods - Hierarchical Clustering
8 Supervised Learning Methods - Nearest Neighbor Algorithm
9 Supervised Learning Methods - Decision Trees
10 Supervised Learning Methods - Naive Bayes
11 Supervised Learning Methods - Regression
12 Application Areas of Generative AI and Ethics
13 Student Project Presentations
14 Student Project Presentations

Recomended or Required Reading

Text Book: Artificial Intelligence A Modern Approach, Stuart Russell, Peter Norvig, Prentice Hall, 1995
Supplementary Book: Artificial Intelligence, George Luger, Addison Wesley, England, 2005

Planned Learning Activities and Teaching Methods

Presentation, Problem Solving, Project

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 * 0.20 + ASG * 0.30 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + ASG * 0.30 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam is 20%, project is 30%, final exam is 50% of the course grade.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr. Zerrin IŞIK
Computer Engineering Department
Dokuz Eylul Unv. Tinaztepe Campus Buca Izmir

zerrin.isik@deu.edu.tr
0232 3017413

Office Hours

To be announced

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 12 12
Preparation for final exam 1 12 12
Project Preparation 1 12 12
Preparing presentations 2 5 10
Final 1 3 3
Midterm 1 2 2
TOTAL WORKLOAD (hours) 93

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.125111
LO.244532
LO.335111
LO.454543

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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