PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
MAT 356	PROBABILITY	3 + 0	3rd Semester	3,5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Compulsory
Course Objective	To teach fundamental concepts of probability. To teach how to construct a probabilistic model to solve real-world problems.
Course Content	Events, sample space, probability; Random variables; Basic probabilistic processes; Discrete-state Markov processes; Propositional logic and proofs; Algebraic structures, Introduction to Graph theory ;Some fundamental limit theorems; Introduction to statistics.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Knows the fundamental concepts of probability.
2	Knows how to construct a probabilistic model in order to solve real-world problems.
3	Knows how to solve problems by using basic probabilistic processes.
4	Knows to make statistical analysis.

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	5	3	1	1	2		4
LO 002	4	2	1	1	1		3
LO 003	4	2	1	1	1		3
LO 004	3	1	1	1			2
Sub Total	16	8	4	4	4		12
Contribution	4	2	1	1	1	0	3	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)	12	3	36
Mid-terms	1	5	5
Final examination	1	8	8
Total Work Load ECTS Credit of the Course			91 3,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Fall	2	FADİME GÖKÇE
Details	2023-2024 Fall	4	FADİME GÖKÇE

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
MAT 356	PROBABILITY	3 + 0	2	Turkish	2023-2024 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. FADİME GÖKÇE	fgokce@pau.edu.tr		MUH A0012	%70

Goals		To teach fundamental concepts of probability. To teach how to construct a probabilistic model to solve real-world problems.
Content		Events, sample space, probability; Random variables; Basic probabilistic processes; Discrete-state Markov processes; Propositional logic and proofs; Algebraic structures, Introduction to Graph theory ;Some fundamental limit theorems; Introduction to statistics.

Topics

Weeks	Topics
1	Introduction to probalility
2	Probabilistic events
3	Sample space and probability
4	Random variables
5	Random variables and distributions
6	Probability processes
7	Markov processes
8	Discrete Markov processes
9	Fundamental limit theorems
10	Central limit theorems
11	Statistical variables
12	Experimental statistics
13	Hypothesis testing
14	Maksimum likelihood and Baye's estimations

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