DEPARTMENT OF SOFTWARE ENGINEERING (ENGLISH)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

1400211013

Ders İsmi:

Probability and Statistics

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	6

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Necessary

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Assoc. Prof. Esengül SALTÜRK

Course Lecturer(s):

Assoc. Prof. Esengül SALTÜRK

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

The aim of the course is to introduce students the basic principals of probability and statistics so that the students should use them in engineering applications such as machine learning, artificial intelligence, computer graphics, randomized algorithms and image processing etc.

Course Content:

Foundations of Probability. Sample spaces, probabilities and distributions, discrete and continuous random variables, expectation, joint probabilities and independence.
Limit Theorems. Law of large numbers, central limit theorem.
Basic Statistics. Mean, variance, covariance, correlation.
Regression. Linear models, least squares estimation.
Hypothesis Testing. Null hypothesis, test statistics, type I and II errors, t-tests.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Understand the language and core concepts of probability theory including combinatorics, independence, conditional probability and Bayes' Theorem.
2) Learn the basic principles of statistical inference.
3) Understand the connection between probability and statistics.
2 - Skills
Cognitive - Practical
1) Use common discrete and continuous probability functions.
2) Learn confidence intervals and perform statistical inference such as hypothesis testing and regression.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Introduction. Sample Spaces. Events. Probability.	Chapter 1
2)	Conditional Probability. Total Probability. Independence. Bayes' Rule.	Chapter 1
3)	Discrete Random Variables. Distributions.	Chapter 2
4)	Discrete Random Variables and Distributions	Chapter 2
5)	Continuous Random Variables and Distributions	Chapter 2
6)	Continuous Random Variables and Distributions	Chapter 2
7)	Expectation. Variance.	Chapter 3
8)	Midterm Exam
9)	Covariance and Correlation.	Chapter 3
10)	Sampling Distributions and Limits	Chapter 4
11)	Statistical Inference.	Chapter 5
12)	Estimation and Bias. Confidence Intervals.	Chapter 6
13)	Bayesian Inference. Hypothesis Testing.	Chapter 7
14)	Linear Regression.	Chapter 10
15)	Linear Regression. Review	Chapter 10
16)	Final Exam

Sources

Course Notes / Textbooks:	1. (Textbook) Probability and Statistics - The Science of Uncertainty, M.J. Evans and J.S. Rosenthal, W. H. Freeman; Second edition, 2023. 2. Applied Statistics and Probability for Engineers, Fifth Edition, Douglas C. Montgomery, George C. Runger, 2011. ISBN-13: 978-0-47005-304-1. 3. For Programming Language: Python for Probability, Statistics and Machine Learning, Jose Unpingco, Springer, 2016.
References:	1. (Textbook) Probability and Statistics - The Science of Uncertainty, M.J. Evans and J.S. Rosenthal, W. H. Freeman; Second edition, 2023. 2. Applied Statistics and Probability for Engineers, Fifth Edition, Douglas C. Montgomery, George C. Runger, 2011. ISBN-13: 978-0-47005-304-1. 3. For Programming Language: Python for Probability, Statistics and Machine Learning, Jose Unpingco, Springer, 2016.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	3	5	2	4
Program Outcomes
1) Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.
2) Skills to design and conduct experiments, collect data, analyze and interpret results.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5) Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6) Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9) Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10) Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11) Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.	5
2)	Skills to design and conduct experiments, collect data, analyze and interpret results.	5
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.	4
4)	Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5)	Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6)	Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7)	Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9)	Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10)	Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11)	Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Alan Çalışması
Anlatım
Bireysel çalışma ve ödevi

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 10
Midterms	1	% 30
Semester Final Exam	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	15	3	45
Study Hours Out of Class	20	6	120
Midterms	2	5	10
Final	1	3	3
Total Workload			178