ECTS - Probability and Statistics
Probability and Statistics (IE220) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Probability and Statistics | IE220 | 4. Semester | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Service Courses Taken From Other Departments |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | In this course, students will learn the basic concepts of probability and statistics and their applications to engineering data and problems. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to probability and statistics, conditional probability, random variables and probability distributions, expected value, sampling distributions, one- and two-sample estimation problems, hypothesis testing, comparison of engineering systems/processes, simple and multiple linear regression, time-based stochastic models, and basic interactions between statistics, data science, machine learning, and AI in engineering |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Probabilistic modeling and the role of Statistics in engineering in the age of AI | Montgomery & Runger, Ch. 1 – The Role of Statistics in Engineering; James et al., Ch. 1 – Introduction; Russell & Norvig, Ch. 1 – Introduction |
| 2 | Probability Fundamentals & Conditional Probability | Montgomery & Runger, Ch. 2 – Probability Russell & Norvig, Ch. 12 – Quantifying Uncertainty; Ch. 13 – Probabilistic Reasoning |
| 3 | Modeling engineering systems/processes via random variables | Montgomery & Runger, Ch. 3 – Discrete Random Variables and Probability Distributions; Ch. 4 – Continuous Random Variables and Probability Distributions |
| 4 | Modeling discrete-valued engineering systems/processes | Montgomery & Runger, Ch. 3 – Discrete Random Variables and Probability Distributions |
| 5 | Modeling continuous-valued engineering systems/processes | Montgomery & Runger, Ch. 4 – Continuous Random Variables and Probability Distributions |
| 6 | A case example from renewable energy | Montgomery & Runger, Ch. 2–4 – Probability, Random Variables and Probability Distributions; Instructor-provided renewable energy case/data |
| 7 | Midterm Exam I (Weeks 1-5) Collection of data from an engineering system/process and estimation | Montgomery & Runger, Ch. 1 – The Role of Statistics in Engineering; Ch. 6 – Descriptive Statistics; Ch. 7 – Sampling Distributions and Point Estimation of Parameters |
| 8 | Estimation in engineering systems/processes with case examples | Montgomery & Runger, Ch. 7 – Sampling Distributions and Point Estimation of Parameters; Ch. 8 – Statistical Intervals for a Single Sample; Instructor-provided engineering cases |
| 9 | A case example from renewable energy (Continued) | Montgomery & Runger, Ch. 3–4 – Probability Distributions; Ch. 7 – Point Estimation of Parameters; Instructor-provided renewable energy case/data |
| 10 | Midterm Exam II (Weeks 6-9) Hypothesis Testing | Montgomery & Runger, Ch. 9 – Tests of Hypotheses for a Single Sample |
| 11 | Comparing Engineering Systems/Processes | Montgomery & Runger, Ch. 10 – Statistical Inference for Two Samples |
| 12 | Regression Models | Montgomery & Runger, Ch. 11 – Simple Linear Regression and Correlation; Ch. 12 – Multiple Linear Regression; James et al., Ch. 3 – Linear Regression |
| 13 | Time-based stochastic models in engineering | Russell & Norvig, Ch. 14 – Probabilistic Reasoning over Time; Montgomery & Runger, Ch. 2–4 – Probability and Probability Distributions |
| 14 | From Statistics to AI and Data-Driven Engineering: Some basic interactions | James et al., Ch. 2 – Statistical Learning; Ch. 3 – Linear Regression; Russell & Norvig, Ch. 1 – Introduction |
| 15 | Review | |
| 16 | Final Exam |
Sources
| Course Book | 1. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, 5th Edition, John Wiley and Sons, 2011 |
|---|---|
| Other Sources | 2. James, Witten, Hastie & Tibshirani - An Introduction to Statistical Learning, 2nd Edition, 2023. |
| 3. Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, 2022. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 60 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | |
| Supportive Courses | X |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. | X | ||||
| 3 | Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. | |||||
| 4 | Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. | X | ||||
| 5 | Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering. | X | ||||
| 6 | Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually. | |||||
| 7 | Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
| 8 | Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
| 9 | Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself. | |||||
| 10 | Acts in accordance with ethical principles and has awareness of professional and ethical responsibility. | |||||
| 11 | Has knowledge about the standards used in computer engineering applications. | |||||
| 12 | Has knowledge about workplace practices such as project management, risk management, and change management. | |||||
| 13 | Gains awareness about entrepreneurship and innovation. | |||||
| 14 | Has knowledge about sustainable development. | |||||
| 15 | Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering. | |||||
| 16 | Gains awareness of the legal consequences of engineering solutions. | |||||
| 17 | Analyzes, designs, and expresses numerical computation and digital representation systems. | X | ||||
| 18 | Uses programming languages and appropriate computer engineering concepts to solve computational problems. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 8 | 16 |
| Prepration of Final Exams/Final Jury | 1 | 13 | 13 |
| Total Workload | 125 | ||