ECTS - Pattern Recognition
Pattern Recognition (EE448) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Pattern Recognition | EE448 | 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 Lecturer(s) |
|
| Course Objectives | 1. Instill in the students an understanding of where Pattern Recognition sits in the hierarchy of artificial intelligence and soft computing techniques 2. Develop expertise in various unsupervised learning algorithms such as clustering techniques (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation 3. Provide the student with the ability to apply these techniques in exploratory data analysis |
| Course Learning Outcomes |
The students who succeeded in this course;
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| Course Content | Introduction to the theory of pattern recognition, Bayesian decision theory, Maximum likelihood estimation, Nonparametric estimation, Linear discriminant functions, Support vector machines, Neural networks, Unsupervised learning and Clustering, Applications such as handwriting recognition, lipreading, geological analysis, medical data processing, d |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Pattern Recognition | Glance this week’s topics from the course book |
| 2 | Classifiers based on Bayesian decision theory | Review last week and glance this week’s topics from your course supplements |
| 3 | Classifiers based on Bayesian decision theory | Review last week and glance this week’s topics from your course supplements |
| 4 | Linear classifiers | Review last week and glance this week’s topics from your course supplements |
| 5 | Nonlinear classifiers | Review last week and glance this week’s topics from your course supplements |
| 6 | Nonlinear classifiers | Review last week and glance this week’s topics from your course supplements |
| 7 | Classifier combination | Review last week and glance this week’s topics from your course supplements |
| 8 | Feature selection | Review last week and glance this week’s topics from your course supplements |
| 9 | Feature generation | Review last week and glance this week’s topics from your course supplements |
| 10 | Feature generation | Review last week and glance this week’s topics from your course supplements |
| 11 | Clustering Algorithms, Multidimensional scaling | Review last week and glance this week’s topics from your course supplements |
| 12 | Clustering Algorithms, Multidimensional scaling | Review last week and glance this week’s topics from your course supplements |
| 13 | Case studies: Image and speech processing | Review last week and glance this week’s topics from your course supplements |
| 14 | Case studies: Image and speech processing | Review last week and glance this week’s topics from your course supplements |
Sources
| Course Book | 1. Pattern Recognition, S.Theodoridis and K.Koutroumbas,4th Ed., Academic Press, 2009. |
|---|---|
| Other Sources | 2. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley, 2001. |
| 3. Pattern Recognition and Machine Learning, C.M.Bishop, Springer, 2006. | |
| 4. Introduction to Pattern Recognition A Matlab Approach, S.Theodoridis, A.Pikrakis, K.Koutroumbas, D.Cavouras, Academic Press, 2010. |
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 | 20 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | - | - |
| Toplam | 5 | 60 |
| Percentage of Semester Work | 55 |
|---|---|
| Percentage of Final Work | 45 |
| 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; ability to apply the theoretical and applied knowledge to model and analyze mechatronics engineering problems. | X | ||||
| 2 | Ability to identify, define and formulate problems related to the field and to select and apply appropriate analysis and modeling methods to solve these problems. | X | ||||
| 3 | Ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; ability to apply contemporary design methodologies; 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.) | X | ||||
| 4 | Ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; ability to use information and communications technologies effectively. | X | ||||
| 5 | Ability to design and perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | X | ||||
| 6 | Ability to work effectively on intra-disciplinary and multi-disciplinary teams; 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 including electrical & electronics and computer engineering. | X | ||||
| 7 | Ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. | X | ||||
| 8 | 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. | X | ||||
| 9 | Consciousness on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. | |||||
| 10 | 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, social 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 in the field of mechatronics engineering. | X | ||||
| 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 planning, 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 | 4 | 4 | 16 |
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentation/Seminar Prepration | 1 | 4 | 4 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 2 | 4 |
| Prepration of Final Exams/Final Jury | 1 | 3 | 3 |
| Total Workload | 117 | ||