PATTERN RECOGNITION AND MACHINE LEARNING

EEM612

Course Code: EEM612
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Doctorate Of Science

Year of Study

Semester

ECTS

Name of Lecturer(s)

Associate Professor Selda Güney

Learning Outcomes

At the end of this course, the students;

To know the basic principles of pattern recognition and automatic learning.
To know the methods of feature extraction.
To develop pattern recognition and machine learning programs and gain an experience.
To know how to apply the methods to a specific problem

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

Course Contents

WEEK	TOPICS
1^st Week	Pattern Recognition Basics
2^nd Week	Feature Selection
3^rd Week	Nearest Neighborhood
4^th Week	The Best Prediction
5^th Week	Bayesian Theorem
6^th Week	Artificial Neural Networks
7^th Week	Artificial Neural Networks
8^th Week	Midterm
9^th Week	Support Vector Machines
10^th Week	Support Vector Machines
11^th Week	Decision Tree Learning
12^th Week	Applications on Selected Problems
13^th Week	Applications on Selected Problems
14^th Week	Applications on Selected Problems

Recommended or Required Reading

1. Pattern Classification and Machine Learning, Christopher Bishop, Springer.
2. Pattern Classification 2nd Edition, R. O. Duda, P.E. Hart & D.G. Stork Wiley, 2001

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Project

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	35
Assignment	1	25
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	3	3
Preparation for Quiz	0	0	0
Individual or group work	14	8	112
Preparation for Final exam	1	40	40
Course hours	14	3	42
Preparation for Midterm exam	1	30	30
Laboratory (including preparation)	0	0	0
Final exam	1	3	3
Homework	2	35	70
Total Work Load			300
Total Work Load / 30 (h)			10
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1	X
K2	X	X	X	X
K3		X	X	X
K4		X	X	X
K5				X
K6				X
K7				X
K8
K9	X	X	X	X
K10				X
K11				X