Pattern Recognition (EE448) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Pattern Recognition EE448 0 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
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. Baran Uslu
  • Asst. Prof. Dr. Hakan Tora
Course Assistants
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;
  • Ability to formulate and describe various applications in pattern recognition
  • Ability to understand the Bayesian approach to pattern recognition
  • Ability to mathematically derive, construct, and utilize Bayesian based classifiers and non-Bayesian based classifiers both theoretically and practically
  • Ability to identify the strengths and weakness of different types of classifiers
  • Ability to validate and assess different clustering techniques
  • Ability to apply various dimensionality reduction methods whether through feature selection or feature extraction
  • Ability to use computer tools (such as Matlab) in developing and testing pattern recognition algorithms
  • Ability to complete a term project
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 Adequate knowledge of mathematics, physical sciences and the subjects specific to engineering disciplines; the ability to apply theoretical and practical knowledge of these areas in the solution of complex engineering problems.
2 The ability to define, formulate, and solve complex engineering problems; the ability to select and apply proper analysis and modeling methods for this purpose.
3 The ability to design a complex system, process, device or product under realistic constraints and conditions in such a way as to meet the specific requirements; the ability to apply modern design methods for this purpose.
4 The ability to select, and use modern techniques and tools needed to analyze and solve complex problems encountered in engineering practices; the ability to use information technologies effectively.
5 The ability to design experiments, conduct experiments, gather data, and analyze and interpret results for investigating complex engineering problems or research areas specific to engineering disciplines.
6 The ability to work efficiently in inter-, intra-, and multi-disciplinary teams; the ability to work individually.
7 (a) Sözlü ve yazılı etkin iletişim kurma becerisi; etkin rapor yazma ve yazılı raporları anlama, tasarım ve üretim raporları hazırlayabilme, etkin sunum yapabilme, açık ve anlaşılır talimat verme ve alma becerisi. (b) En az bir yabancı dil bilgisi; bu yabancı dilde etkin rapor yazma ve yazılı raporları anlama, tasarım ve üretim raporları hazırlayabilme, etkin sunum yapabilme, açık ve anlaşılır talimat verme ve alma becerisi.
8 Recognition of the need for lifelong learning; the ability to access information, follow developments in science and technology, and adapt and excel oneself continuously.
9 Acting in conformity with the ethical principles; professional and ethical responsibility and knowledge of the standards employed in engineering applications.
10 Knowledge of business practices such as project management, risk management, and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development.
11 Knowledge of the global and social effects of engineering practices on health, environment, and safety issues, and knowledge of the contemporary issues in engineering areas; awareness of the possible legal consequences of engineering practices.
12 (a) Knowledge of (i) fluid mechanics, (ii) heat transfer, (iii) manufacturing process, (iv) electronics and control, (v) vehicle components design, (vi) vehicle dynamics, (vii) vehicle propulsion/drive and power systems, (viii) technical laws and regulations in automotive engineering field, and (ix) vehicle verification tests. (b) The ability to merge and apply these knowledge in solving multi-disciplinary automotive problems.
13 The ability to make use of theoretical, experimental, and simulation methods, and computer aided design techniques in automotive engineering field.
14 The ability to work in the field of vehicle design and manufacturing.

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