TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ205 |
| Name | Probability and Statistics |
| Term | 2026-2027 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 3-1 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3.5 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. YUSUF ALPER KAPLAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
Introduce the student with probability concepts and their relation to the events.
Course Content
Probability, conditional probability, Bernoulli trials, the concept of a random variable, distribution and density functions, specific random variables, conditional distributions, functions of one random variable, mean and variance, functions of two random variables, conditional expected values, stochastic processes, systems with stochastic inputs, the power spectrum, discrete-time processes, poisson process.
Course Precondition
There is no prerequisite.
Resources
“Statistics for Business and Economics” Paul Newbold, William L. Carlson and Betty Thorne, Upper Saddle River, N.J. : Prentice Hall, cop. 2007, 7th ed.
Notes
Navidi, William (2019) Statistics for Engineers and Scientists, 5th ed., McGraw Hill.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Understand the definitions of probability and conditional probability and the basic concepts of probability. |
| LO02 | Bernoulli trials are studied along with the concept and types of random variables. |
| LO03 | Methods for calculating the expected value and variance of random variables are learned. |
| LO04 | The concept of random functions and their role in probability models are explored. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | |
| PLO04 | - | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to probability | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 2 | Set theory and conditional probability | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 3 | Bernoulli trials | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 4 | Random variables | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 5 | Cumulative distribution function | There is no prerequisite. | Yöntem Seçilmemiş |
| 6 | Probability density function | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 7 | Specific random variables | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Discrete random variables | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 10 | Conditional distributions | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 11 | Asymptotic approximations | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 12 | Functions of one random variable | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 13 | Expected value | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 14 | Functions of two random variables | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 15 | Sample of Problems | There is no prerequisite. | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 1 | 1 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 153 | ||
| Total Workload / 25 (h) | 6,12 | ||
| ECTS | 6 ECTS | ||