COURSE UNIT TITLE

: METHODS OF ARTIFICIAL INTELLIGENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MTS 3005 METHODS OF ARTIFICIAL INTELLIGENCE ELECTIVE 2 0 0 3

Offered By

Faculty of Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR DERYA BIRANT

Offered to

Industrial Engineering
Textile Engineering
Mechanical Engineering
Mechanical Engineering (Evening)
Metallurgical and Materials Engineering
Mining Engineering
Civil Engineering
Environmental Engineering
Civil Engineering (Evening)

Course Objective

The aim of this course is to effectively discuss, teach and apply methods in the field of artificial intelligence; Thus, to ensure that students can increase their analytical and theoretical thinking powers and solve problems effectively. It is also aimed to establish interdisciplinary (multi-disciplinary) teams and carry out a project.

Learning Outcomes of the Course Unit

1   To define the basic concepts of artificial intelligence
2   To learn artificial intelligence methods
3   To learn the applications of artificial intelligence methods in daily life
4   To solve problems with artificial intelligence techniques
5   To work in a multidisciplinary team within the scope of a project

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 History of Artificial Intelligence
3 Applications of Artificial Intelligence
4 Introduction to Learning Techniques
5 Supervised Learning Methods - K-Nearest Neighbor Algorithm
6 Supervised Learning Methods - Naive Bayes
7 Supervised Learning Methods - Decision Trees
8 Solving Artificial Intelligence Problems
9 Unsupervised Learning Methods - Kmeans Clustering
10 Unsupervised Learning Methods - Association Rule Mining
11 Project Presentations Project presentations by multidisciplinary teams
12 Project Presentations Project presentations by multidisciplinary teams
13 Project Presentations Project presentations by multidisciplinary teams
14 Project Presentations Project presentations by multidisciplinary teams

Recomended or Required Reading

Ethem Alpaydın, Yapay Öğrenme: Yeni Yapay Zeka, 2020.

Planned Learning Activities and Teaching Methods

Lecture / Presentation
Problem solving
Project development and presentation by interdisciplinary (multidisciplinary) teams

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.15 + PRJ * 0.35 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.15 + PRJ * 0.35 + RST * 0.50


Further Notes About Assessment Methods

The project will be developed with interdisciplinary (multidisciplinary) teams.

Assessment Criteria

LO1, LO2, LO4 are evaluated with exams.
LO3, LO5 are assessed by the project.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to class is mandatory.

Contact Details for the Lecturer(s)

Prof.Dr. Derya BIRANT
E-mail: derya@cs.deu.edu.tr

Office Hours

It will 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 6 6
Preparation for final exam 1 8 8
Project Preparation 1 17 17
Midterm 1 1 1
Final 1 1 1
TOTAL WORKLOAD (hours) 75

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.13
LO.23
LO.33
LO.43
LO.5443

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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