| ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| INE2002 | Statistics in Engineering | Spring | 3 | 2 | 4 | 7 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. SABRİ TANKUT ATAN |
| Course Lecturer(s): |
Prof. Dr. SELİM ZAİM Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU Prof. Dr. CENGİZ KAHRAMAN RA ESRA ADIYEKE |
| Course Objectives: | The aim of the course is to provide the fundamentals of engineering statistics such as random sampling, data analysis, sampling distribution theory, estimation, confidence intervals, hypothesis tests, goodness of fit tests, and regression and correlation analysis. |
|
The students who have succeeded in this course; I. Explain the concept of random sampling, evaluate sample mean and sample variance, construct and interpret visual data displays (stem-and-leaf display, histogram, box plot) and normal probability plot II. Explain statistical inference, point estimation, interval estimation. Use the Central Limit Theorem to identify the sampling distributions of means computed from samples. Determine the distribution of point estimators. Construct certain confidence intervals and interpret the results. III. Explain hypothesis testing and error types. Generates hypothesis testing procedure and interprets the results. IV. Construct certain confidence intervals and hypothesis tests for comparing two samples, and interpret the results. V. Construct and analyze simple linear regression models. |
| Population vs sample, sample mean, sample variance, stem-and-leaf display, histogram, box plot, normal probability plot, sampling distributions and point estimation of parameters, statistical intervals for a single sample, tests of hypotheses for a single sample, statistical inference for two samples, linear regression. |
| Week | Subject | Related Preparation | |
| 1) | Introduction to statistics. | ||
| 2) | Population vs sample, sample mean, sample variance. | ||
| 3) | Stem-and-leaf display, histogram, box plot, normal probability plot | ||
| 4) | Sampling distributions and point estimation of parameters | ||
| 5) | Sampling distributions and point estimation of parameters | ||
| 6) | Statistical intervals for a single sample | ||
| 7) | Statistical intervals for a single sample | ||
| 8) | Tests of hypotheses for a single sample | ||
| 9) | Midterm | ||
| 10) | Statistical inference for two samples | ||
| 11) | Statistical inference for two samples | ||
| 12) | Simple linear regression | ||
| 13) | Simple linear regression | ||
| 14) | Nonparametric tests | ||
| Course Notes: | Allan G. Bluman, Elementary Statistics, McGraw Hill, Most recent edition. |
| References: | • Douglas C. Montgomery and George C. Runger. Applied Statistics and Probability for Engineers, John Wiley & Sons, 7th Edition. • Ross, S. M. (2017). Introductory Statistics. Academic Press, 4th Edition. • Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press. 5th Edition. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 12 | % 20 |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 20 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 14 | 1 | 14 |
| Application | 14 | 1 | 14 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 1 | 20 | 20 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 1 | 15 | 15 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 30 | 30 |
| Total Workload | 177 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | 5 |
| 2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | 5 |
| 3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | 5 |
| 4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | 5 |
| 5) | Select and use modern techniques and tools necessary for engineering applications. | 5 |
| 6) | Design and conduct experiments, collect data, and analyse and interpret results. | 5 |
| 7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
| 8) | Access information via conducting literature research, using databases and other resources | |
| 9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
| 10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
| 11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
| 12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
| 13) | Have a sense of professional and ethical responsibility. | |
| 14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |