1	Course Title:	R PROGRAMMING AND MACHINE LEARNING APPLICATIONS
2	Course Code:	EEM4122
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	4
6	Semester:	8
7	ECTS Credits Allocated:	5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. GIYASETTİN ÖZCAN
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Bilgisayar Müh. Bölüm Binası, 1. kat, oda 107 Tel.:+90 (224) 294 2792 email: gozcan at uludag.edu.tr
18	Website:
19	Objective of the Course:	To learn the logics of R programming. To learn fundamental concepts of R programming. To learn the concepts of CRAN and Bioconductor packages. To learn to implement machine learning applications
20	Contribution of the Course to Professional Development	Writing general purpose R program codes

21

Learning Outcomes:

1 Students will learn to install R and R studio; 2 Students will learn to exploit CRAN and Bioconductor packages.; 3 Students will be able to write machine learning implementations with R.;

22	Course Content:

Week	Theoretical	Practical
1	R and R Studio Installation, basic implementation
2	Fundamental artihmetic operators
3	Variables
4	For, if loops
5	While, switch loops
6	R functions
7	Vector, matrice
8	Data frame, factor
9	CRAN packages, graphics
10	Machine learning implementations
11	Bioconductor packages
12	Bioinformatics implementations
13	Deep learning packages
14	Deep learning implementations

23	Textbooks, References and/or Other Materials:	Lecture Notes
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	35
Quiz	0	0
Homeworks, Performances	2	5
Final Exam	1	60
Total	4	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 priciples of Bursa uludag University Associate and Undergraduate Education Regulation.
Information	The relative evoluation system is applied. 1 Midterm and 1 Final exams are held.

25

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	0	0	0
Homeworks, Performances	2	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	50	50
Others	0	0	0
Final Exams	1	58	58
Total WorkLoad			200
Total workload/ 30 hr			5
ECTS Credit of the Course			5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 5 5 3 3 2 1 1 1 1 1 1 1 LO2 5 2 2 3 2 1 1 1 1 1 1 1 LO3 5 5 3 3 2 1 1 1 1 1 1 1

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High