Course Details

PROBABILITY AND STATISTICS

MATH301

Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
5MATH301PROBABILITY AND STATISTICS3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The course provides an introduction to probability and statistics. The goal is to teach students powerful analytical and numerical tools in the areas of probability and statistics that can be used to solve real world engineering problems. Lectures will be supplemented by programming exercises and will include practical examples.
Course Content Principles of counting, permutations, combinations
Probability and Bayes’ theorem
Random variables
Expectation
Central limit theorem and the law of large numbers
Joint distributions, covariance and correlation
Descriptive statistics
Maximum likelihood estimation
Bayesian inference
Bayesian updating with discrete priors
Bayesian updating with continuous priors
Beta distributions, conjugate priors, probability intervals
Frequentist inference—Null hypothesis significance testing
Confidence intervals
Course Methods and Techniques Grading Policy
The final grades will be computed based on the general performance of the class and the distribution of grades (i.e. who deserves A and who deserves F). The grading strategy will be a combination of the standard catalogue grading and curve grading.

Attendance Policy
Attendance will not be taken and is not part of grading.

Late Submission Policy
It is the student s responsibility to follow the classes and do the quiz and exams on time. Late homework submissions will be subject to a penalty of 25% if submitted within one week after the due date and %50 if submitted after one week.

Make-Up Policy
There are no make-ups in homework assignments and quizzes. The student may be exempt from these assignments if a written and formal documentation is provided. Possible reasons for excused absences include serious illnesses, illness or death of a family member, university related trips and other serious circumstances. Acceptable documents for claiming an excused absence include medical doctor’s statements, petitions related to official university travels, court related documents, etc. If the student misses an exam (midterms or final) he or she can take a make-up exam upon submitting a formal document or can be exempt from the exams.
Prerequisites and co-requisities ( MATH152 )
Course Coordinator Associate Prof.Dr. Zafer Aydın zafer.aydin@agu.edu.tr
Name of Lecturers Associate Prof.Dr. ZAFER AYDIN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources
https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/
https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/
https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/
https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/

Course Category
Mathematics and Basic Sciences %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 1 % 30
Quiz/Küçük Sınav 5 % 20
Ödev 4 % 20
Final examination 1 % 30
Total
11
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 2 2
F2F Dersi 14 3 42
Ev Ödevi 5 3 15
Kısa Sınav 1 1 1
Okuma 14 1 14
Kişisel Çalışma 2 20 40
Ders dışı çalışma 14 2 28
Final Sınavı 1 3 3
Total Work Load   Number of ECTS Credits 5 145

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain basic concepts in probability and statistics
2 Solve basic problems arising in engineering that involve discrete and continuous probability distributions
3 Perform statistical inference using confidence intervals and hypothesis testing
4 Solve a probability or statistics problem using a software by writing programs, doing simulations and generating graphs,


Weekly Detailed Course Contents
Veri yok


Contribution of Learning Outcomes to Programme Outcomes
P1
C1
C2
C3
C4

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74951&lang=en