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 | 5. 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 | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| 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 | Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. | X | ||||
| 2 | An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. | X | ||||
| 3 | An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.) | |||||
| 4 | An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. | X | ||||
| 5 | An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | X | ||||
| 6 | An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines. | |||||
| 7 | An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. | |||||
| 8 | An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation. | |||||
| 9 | Consciousness on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. | |||||
| 10 | A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project. | |||||
| 11 | Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions. | |||||
| 12 | Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions. | |||||
| 13 | Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism. | |||||
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 | ||