ECTS - Pattern Classification and Sensor Applications for Engineers

Pattern Classification and Sensor Applications for Engineers (EE449) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Pattern Classification and Sensor Applications for Engineers EE449 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Hakan TORA
Course Assistants
Course Objectives Sensors, general information about sensor types and sensor working principles. What is a pattern? Pattern classification applications. Theory and methods of pattern classification. Feature extraction and selection. MATLAB Classification Learner Tool. Analysis and performance of classifiers. RFID basics.
Course Learning Outcomes The students who succeeded in this course;
  • Know about sensors.
  • Design a classifier system.
  • Analyze the performance of classifiers.
  • Design and implement a project including sensors.
  • Use the MATLAB Classification Learner application tool.
Course Content Sensors, general information about sensor types and sensor working principles; what is a pattern; pattern classification applications; theory and methods of pattern classification; feature extraction and selection; MATLAB Classification Learner Tool; analysis and performance of classifiers; RFID basics.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 What is a sensor? Obtain the reference books.
2 Sensor types. Review last week’s topics
3 Working principles of sensors. Review last week’s topics
4 What is a pattern? Review last week’s topics
5 Theory of pattern classification Review last week’s topics
6 Feature extraction. Review last week’s topics
7 Feature selection. Review last week’s topics
8 Analysis and performance of classifiers. Review last week’s topics
9 Midterm Exam Review all topics up-to this week
10 Design of an interdisciplinary project Review all topics
11 Project work continued Review all topics
12 Implementation of the project. Review all topics
13 Implementation of the project Review all topics
14 Presentations Review the project

Sources

Course Book 1. Duda, R. O., & Hart, P. E. (2006). 2nd Edition, Pattern classification. John Wiley & Sons.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 15
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 35
Toplam 6 100
Percentage of Semester Work
Percentage of Final Work 100
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 Adequate knowledge of subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems. X
2 An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this. X
3 An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this. X
4 The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively
5 Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics.
6 An ability to function on multi-disciplinary teams, and ability of individual working. X
7 Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions.
8 Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated.
9 Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications.
10 Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development.
11 Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

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 1 20 20
Report
Homework Assignments 3 3 9
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 3 3
Prepration of Final Exams/Final Jury 1 3 3
Total Workload 131