Pattern Recognition (EE448) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Pattern Recognition EE448 3 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)
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 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 X
5 Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics. X
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. X
8 Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated. X
9 Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications. X
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. X
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. X

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