Pattern Recognition (EE448) Ders Detayları

Course Name Corse Code Dönemi Lecture Hours Uygulama Saati Lab Hours Credit ECTS
Pattern Recognition EE448 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language İngilizce
Course Type Technical Elective Courses
Course Level Lisans
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
Major Area Courses X
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; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. X
2 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. X
3 An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an 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 An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. X
5 An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. X
6 An ability to work effectively on single disciplinary and multi-disciplinary teams; an 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. X
7 An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. X
8 An 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. X
10 A 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. X
11 Knowledge about the global, societal 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. X
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. 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 planing, improving or changing the norms with a criticism. 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