GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION

MACHINE LEARNING/5201305

COURSE INFO

-- LANGUAGE OF INSTRUCTION

Turkish

-- NAME OF LECTURER(S)

Dr.Oktay YILDIZ

-- WEB SITE(S) OF LECTURER(S)

http://w3.gazi.edu.tr/~oyildiz/

-- EMAIL(S) OF LECTURER(S)

oyildiz@gazi.edu.tr

-- LEARNING OUTCOMES OF THE COURSE UNIT

To understand the basic machine learning techniques and algorithms and to apply them to real-world problems.

-- MODE OF DELIVERY

The mode of delivery of this course is Face to face

-- PREREQUISITES AND CO-REQUISITES

There is no prerequisite or co-requisite for this course.

-- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS

There is no recommended optional programme component for this course.

--COURSE CONTENT
1. Week	Introduction to machine learning
2. Week	Concept Learning
3. Week	Decision Tree
4. Week	Genetic Algorithms
5. Week	Genetic programming
6. Week	Project Presentations
7. Week	Bayesian learning
8. Week	Midterm
9. Week	Neural networks
10. Week	Project Presentations
11. Week	Support Vector Machines
12. Week	Evaluation of learning algorithms
13. Week	Unsupervised Learning
14. Week	Project Presentations
15. Week	Project Presentations
16. Week	Project Presentations

-- RECOMMENDED OR REQUIRED READING

Machine Learning, Tom Mitchell

-- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS

Lecture, Question & Answer, Demonstration, Drill - Practise

-- WORK PLACEMENT(S)

Not Applicable

-- ASSESSMENT METHODS AND CRITERIA

	Quantity	Percentage
Mid-terms	1	20
Assignment	4	10
Exercises	0	0
Projects	1	10
Practice	0	0
Quiz	0	0
Contribution of In-term Studies to Overall Grade		40
Contribution of Final Examination to Overall Grade		60

-- WORKLOAD

Efficiency	Total Week Count	Weekly Duration (in hour)	Total Workload in Semester
Theoretical Study Hours of Course Per Week	15	3	45
Practising Hours of Course Per Week			0
Reading	15	3	45
Searching in Internet and Library	15	3	45
Designing and Applying Materials			0
Preparing Reports	1	6	6
Preparing Presentation	1	6	6
Presentation	1	1	1
Mid-Term and Studying for Mid-Term	4	4	16
Final and Studying for Final	4	4	16
Other	1	8	8
TOTAL WORKLOAD:			188
TOTAL WORKLOAD / 25:			7.52
ECTS:			8

-- COURSE'S CONTRIBUTION TO PROGRAM

NO	PROGRAM LEARNING OUTCOMES	1	2	3	4	5
1	Improves and deepens the field knowledge at an expert level based on undergraduate proficiency.				X
2	Comprehends the interactions between the computer science and other related disciplines.			X
3	Uses expert level theoretical and practical knowledge acquired in the computer science field.			X
4	Creates new knowledge by integrating the computer science knowledge and the knowledge from related disciplines.			X
5	Defines a problem in the computer science field.			X
6	Analyses the problems in the computer science field by using scientific research methods.			X
7	Proposes solutions to the problems in the computer science field.			X
8	Solves problems in the computer science field.			X
9	Evaluates the results within the perspectives of quality processes.	X
10	Develops new approaches and methods by taking responsibility in complex situations in the application stages.	X

Gazi University