Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
CSE 5007	MACHINE LEARNING AND REASONING	ELECTIVE	3	0	0	9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR RECEP ALP KUT

Offered to

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

Course Objective

Machine learning algorithms play an important role in industrial applications, commercial data analysis and especially data mining applications. The aim of this course is to give students both the theoretical justification and practical application of machine learning algorithms on real-world data sets. This course is intended for graduate students who conduct research in fields which use machine learning, such as computer vision, natural language processing, data mining, bioinformatics, and robotics.

Learning Outcomes of the Course Unit

1	Define three styles of learning: Supervised, Reinforcement, and Unsupervised
2	Define basic learning techniques such as Decision Trees, Bayesian Learning, Neural Networks, Genetic Algorithms etc.
3	Determine which machine learning technique is appropriate to solve a particular problem
4	Design a machine learning model
5	Implement a simple machine learning 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 Machine Learning
2	Basic concepts and issues in Machine Learning
3	Learning (Supervised, Unsupervised, Reinforcement)
4	Real world machine learning applications
5	Bayesian Learning: Bayes Theorem, Naive Bayes Classifier
6	Decision Tree Learning
7	Artificial Neural Networks
8	Genetic Algorithm
9	Instance Based Learning: K-Nearest Neighbor
10	Reinforcement Learning
11	Unsupervised Learning: Clustering
12	Self Organizing Maps
13	Presentations
14	Presentations

Recomended or Required Reading

Textbook(s): Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, first edition 2004, second edition 2010.

Supplementary Book(s): Igor Kononenko, Matjaz Kukar (2007) Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood Publishing Limited, 454 pages.

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	PRS	PRESENTATION
3	FCG	FINAL COURSE GRADE	ASG * 0.50 + PRS * 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)

Prof.Dr. R. Alp KUT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 01
e-mail: alp@cs.deu.edu.tr

Office Hours

Thursday 9:00 - 10:00

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
Project Preparation	1	65	65
Preparing presentations	1	50	50
Preparing report	1	40	40
TOTAL WORKLOAD (hours)			225

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.10
LO.1	4	3						3
LO.2	4	5						3
LO.3					5		1
LO.4			4					1	2
LO.5			4	2		1

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION