Graduate Study - Faculty of Engineering and Natural Sciences
PhD Information Technology

Code Name Level Year Semester
CEN 671 Special Topics in Pattern Recognition Graduate 1 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
7.5 0 english

Instructor Assistant Coordinator
Abdülhamit Subaşı, Prof. Dr. Abdülhamit Subaşı, Prof. Dr.
[email protected] no email

This course deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modelling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

  1. Introduction to statistical pattern recognition
  2. Bayesian decision theory, Maximum likelihood and Bayesian parameter estimation
  3. Linear Classifiers, Nonlinear Classifiers
  4. Feature Selection and Extraction
  5. Parametric techniques, Nonparametric techniques
  6. Linear Discriminant Functions
  7. Tree Based Methods
  8. Multilayer Neural Networks
  9. Stochastic Methods
  10. Non-metric Methods
  11. Algorithm-independent machine learning
  12. Unsupervised Learning and Clustering
  13. Presentations
  14. Presentations


    • Lectures
    • Presentation
    • Project
    Description (%)
    Method Quantity Percentage (%)
    Term Paper130
    Final Exam140
    Total: 100
    Learning outcomes
    • Demonstrate a systematic and critical understanding of the theories, principles and practices of pattern recognition;
    • Creatively apply contemporary theories, processes and tools in the development and evaluation of solutions to problems in pattern recognition;
    • Actively participate in, reflect upon, and take responsibility for, personal learning and development, within a framework of lifelong learning and continued professional development;
    • Present issues and solutions in appropriate form to communicate effectively with peers and clients from specialist and non-specialist backgrounds;
    • Work with minimum supervision, both individually and as a part of a team, demonstrating the interpersonal, organisation and problem-solving skills supported by related attitudes necessary to undertake employment.
    • S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Introduction to Pattern Recognition A MATLAB® Approach, Academic Press, Elsevier Inc. 2010.

    ECTS (Allocated based on student) WORKLOAD
    Activities Quantity Duration (Hour) Total Work Load
    Lecture (14 weeks x Lecture hours per week) 0
    Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) 0
    Midterm Examination (1 week) 0
    Final Examination(1 week) 0
    Preparation for Midterm Examination 0
    Preparation for Final Examination7.50
    Total Workload: 0
    ECTS Credit (Total workload/25): 0