COURSE UNIT TITLE

: DATA MINING APPLICATIONS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
END 3966 DATA MINING APPLICATIONS ELECTIVE 3 0 0 5

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

With this proposed course, it is aimed to explain to DEU Industrial Engineering Department students how data, which has an extremely important place in engineering science and real-life problems, can be used and how information can be produced from data, using data mining and machine learning methods. In this course, our students will be given basic information about data mining methods and in-class applications will be made with the help of the free data mining software program Weka. Within the scope of the course, studies will be carried out on data mining and machine learning approaches, which are the basic sub-topics of artificial intelligence.

Learning Outcomes of the Course Unit

1   Being able to understand and apply data mining approaches.
2   Ability to filter and visualize data.
3   Ability to apply basic data mining techniques with Weka.
4   Understanding and applying machine learning.
5   Ability to use basic data mining and machine learning algorithms.
6   Obtaining information on legal liability and ethics in data mining approaches.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Data Mining
2 Weka software introduction
3 Filtering, visualization of data and data pre-processing with Weka
4 Working on classifiers, baseline accuracy concept
5 Cross validation
6 Simple classifiers, concept of overfitting
7 Decision trees
8 Decision trees
9 K-Nearest neighborhoof algorithm
10 Classification and linear regression
11 Classification by Regression, Logistic Regression
12 Clustering
13 Support vector machines
14 Data mining process, pitfalls, ethics, period review

Recomended or Required Reading

Witten, Ian H., Eibe Frank, and A. Mark. "Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques.", ISBN: 978-0128042915

Planned Learning Activities and Teaching Methods

Inclass avtivities and applications

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 (%20) + Project(%30) + Final (%50)

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

burcin.ozsoydan@deu.edu.tr

Office Hours

To be announced.

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
Preparation for midterm exam 1 20 20
Preparation for final exam 1 20 20
Preparing presentations 1 15 15
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 129

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.1444
LO.24
LO.3444
LO.444
LO.544
LO.633

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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