1	Course Title:	MACHINE LEARNING
2	Course Code:	BLPS2414
3	Type of Course:	Optional
4	Level of Course:	Short Cycle
5	Year of Study:	2
6	Semester:	4
7	ECTS Credits Allocated:	3
8	Theoretical (hour/week):	2
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Öğr. Gör. AHMET DARTAR
16	Course Lecturers:	--
17	Contactinformation of the Course Coordinator:	ahmetdartar@uludag.edu.tr, (0 224) 294 26 62, Bursa Uludağ Üniversitesi Karacabey MYO Bilgisayar Programcılığı
18	Website:
19	Objective of the Course:	The aim of this course is to provide students with the theoretical basis of machine learning algorithms and practical application of them on real-world data sets.
20	Contribution of the Course to Professional Development	For a problem whose parameters are given, the student can reveal the advantages and disadvantages of different machine learning methods.

21

Learning Outcomes:

1 Describe basic machine learning concepts; 2 Solve a particular problem that includes one of the learning types; 3 Apply machine learning techniques on given dataset; 4 Develop a project with use of a machine learning approach; 5 Evaluate a leaning model;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to Machine Learning
2	Applications of Machine Learning
3	Data Digitization
4	Feature Selection/Extraction
5	Regression Algorithms
6	Classification Algorithms (Support Vector Machine)
7	Classification Algorithms (Artificial Neural Network)
8	Mid-term exam
9	Classification Algorithms (K-nearest Neighbor Algorithm)
10	Classification Algorithms (Naive Bayes Algorithm)
11	Classification Algorithms (Decision Tree)
12	Clustering Algorithms (K-Means Algorithm)
13	Clustering Algorithms (Single Linkage Clustering Algorithm-SLINK/Complete Linkage Clustering Algorithm-CLINK)
14	Ensemble Learning Algorithms and Classifier Performance

23	Textbooks, References and/or Other Materials:	1-Ethem ALPAYDIN (2010). Introduction to Machine Learning, The MIT Press, second edition. 2-Tom Mitchell,McGraw-Hill. Machine Learning. ISBN 0070428077. 3-Atınç Yılmaz, Makine Öğrenmesi: Teorisi ve Algoritmaları, Papatya Bilim Yayınevi, 2018
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation is carried out according to the principles of Bursa uludag University Associate and Undergraduate Education
Information	Results are determined with the letter grade determined by the student automation system.

25

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	2	28
Practicals/Labs	0	0	0
Self Study and Preparation	14	2	28
Homeworks, Performances	0	2	28
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	3	3
Others	0	0	0
Final Exams	1	3	3
Total WorkLoad			93
Total workload/ 30 hr			3
ECTS Credit of the Course			3

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 LO1 2 4 2 5 3 3 3 2 2 3 2 LO2 2 4 2 5 3 3 3 2 2 2 2 LO3 2 4 2 5 3 3 2 2 2 2 2 LO4 4 5 2 4 3 4 2 3 3 3 3 LO5 2 4 2 5 3 3 3 2 2 3 2

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High