PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ELK 520	MACHINE LEARNING	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	To teach machine learning techniques. To implement these techniques in MatLab environment. To use these techniques in solving real-world problems.
Course Content	Classification / Regression / Support Vector Machines and Applications
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Knows fundamental concepts about machine learning
2	Knows machine learning structures
3	Can solve real world problem by using ML tools
4	Can make modeling and prediction by ML tools

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001	2	5	4	4	4
LO 002	2	5	4	4	4
LO 003	2	4	5	5	5
LO 004	2	4	5	5	5
Sub Total	8	18	18	18	18
Contribution	2	5	5	5	5	0	0	0	0	0	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	50	50
Final examination	1	61	61
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ELK 520	MACHINE LEARNING	3 + 0	1	Turkish	2018-2019 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SERDAR İPLİKÇİ	iplikci@pau.edu.tr		MUH A0401	%

Goals		To teach machine learning techniques. To implement these techniques in MatLab environment. To use these techniques in solving real-world problems.
Content		Classification / Regression / Support Vector Machines and Applications

Topics

Weeks	Topics
1	Introduction
2	Constrained Optimization
3	Constrained Optimization
4	Constrained Optimization
5	Classification by Support Vector Machines
6	Classification by Support Vector Machines
7	Classification by Support Vector Machines
8	Classification by Support Vector Machines
9	Regression by Support Vector Machines
10	Regression by Support Vector Machines
11	Regression by Support Vector Machines
12	Regression by Support Vector Machines
13	Applications
14	Applications

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student