| Course Name | Pattern Recognition |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| CE 322 | Fall/Spring | 3 | 0 | 3 | 5 |
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | Elective | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | ||||||
| Course Coordinator | ||||||
| Course Lecturer(s) | - | |||||
| Assistant(s) | - | |||||
| Course Objectives | The course focuses on the theory and applications of pattern recognition. The topics include an overview of the problem of pattern classification, feature extraction, object recognition, statistical decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | The following topics will be included: learning and adoption, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, sufficient statistics, non-parametric techniques, linear discriminants, algorithm independent machine learning, classifiers, unsupervised learning, clustering. |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction to Pattern Recognition | Chapter 1.Sections 1.1-1.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 2 | Learning and Adoption | Chapter 1.Sections 1.5,1.6. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 3 | Bayesian Decision Theory | Chapter 2.Sections 2.1-2.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 4 | Discriminant Functions | Chapter 2.Sections 2.5,2.6, 2.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 5 | Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation | Chapter 3.Sections 3.1-3.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 6 | Sufficient Statistics | Chapter 3.Sections 3.5-3.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 7 | Non-Parametric Techniques | Chapter 4.Sections 4.1-4.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 8 | Linear Discriminant Functions | Chapter 5.Sections 5.1-5.8. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 9 | Midterm Exam | |
| 10 | Non-MetricMethods | Chapter 8.Sections 8.1-8.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 11 | Algorithm-Independent Machine Learning | Chapter 9.Sections 9.1-9.3. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 12 | Algorithm-Independent Machine Learning – Resampling | Chapter 9.Sections 9.4,9.5. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 13 | Algorithm-Independent Machine Learning – Classifiers | Chapter 9.Sections 9.6,9.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 14 | Unsupervised Learning and Clustering | Chapter 10.Sections 10.1-10.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 15 | Unsupervised Learning and Clustering | Chapter 10.Sections 10.5-10.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| 16 | Project Presentations |
| Course Notes/Textbooks | Duda, R.O.Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
| Suggested Readings/Materials | Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007; Marsland, S. Machine Learning: An Algorithmic Perspective. CRC Press. 2009. (Also uses Python.); Theodoridis, S. and Koutroumbas, K. Pattern Recognition. Edition 4. Academic Press, 2008. |
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | 5 | 10 |
| Presentation / Jury | ||
| Project | 1 | 20 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 30 |
| Final Exam | 1 | 40 |
| Total |
| Weighting of Semester Activities on the Final Grade | 7 | 60 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 14 | 2 | 28 |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | 5 | 2 | |
| Presentation / Jury | |||
| Project | 1 | 20 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 20 | |
| Final Exams | 1 | 24 | |
| Total | 150 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems | |||||
| 2 | To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. | |||||
| 3 | To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose. | |||||
| 4 | To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively. | |||||
| 5 | To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems. | |||||
| 6 | To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually. | |||||
| 7 | To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. | |||||
| 8 | To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. | |||||
| 9 | To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications. | |||||
| 10 | To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
| 11 | Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1) | |||||
| 12 | To be able to use the second foreign language at intermediate level. | |||||
| 13 | To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
