Module/Course Title: Pattern Recognition

Module course code

KOMS120608

Student Workload
119 hours

Credits

3 / 4.5 ETCS

Semester

6

Frequency

Even Semester

Duration

16

1

Type of course

Field of Study Courses

Contact hours


40 hours of face-to-face (theoretical) class activity

Independent Study


48 hours of independent activity
48 hours of structured activities

Class Size

30

2

Prerequisites for participation (if applicable)

-

3

Learning Outcomes

  1. Students can demonstrate systematic thinking in analyzing and designing intelligent system solutions
  2. Students can apply effective methods in developing intelligent systems
  3. Students can create and evaluate intelligent systems
  4. Students can select feature selection needed for pattern recognition
  5. Students can calculate distance using methods to maximize recognition accuracy
  6. Students can compute clustering using one kind of methods or algorithms
  7. Students can classify patterns using suitable methods or algorithms
  8. Students can collect data sets for classification and clustering

4

Subject aims/Content

The pattern recognition course discusses pattern recognition and regularity in data. This course aims to allow students to identify and analyze data regularly. Students will learn how to extract meaningful information from data features. This course involves statistical and information theory concepts related to machine learning, data mining, and pattern recognition. Objects are assigned to classes to which they are most similar in the process of machine perception, known as pattern recognition, which deals with the problem of identifying and categorizing patterns in data. The three contemporary pattern recognition methods—statistical, structural, and neural—are introduced in this course. Pattern recognition uses machine learning methods to identify patterns and regularities in data automatically. Text, images, sounds, or other recognizable elements may include this information. Systems for pattern recognition can quickly and correctly identify well-known patterns. The materials discussed in this course include basic concepts of pattern recognition. The classical pattern recognition model involves three major operations— representation, feature extraction, and classification.

Study Material

MIDTERM EXAM

FINAL EXAM

Basic Pattern Recognition

Basic Pattern Recognition

Feature Selection

Feature Extraction

Distance Measurement

 

Clustering

 

  • Linear Discriminant
  • Cluster Validation

 

Bayesian Decision Theory

Preprocessing

Classification cycle

Structural Approach: Template Matching

Statictical Approach: Hidden Markov Model (HMM)

Statistical Approach: Boltzmann

End of Semester Project Socialization

5

Teaching methods

-

6

Assesment Methods

  • Presence;
    Midterm exam

7

This module/course is used in the following study programme/s as well

Computer Science Study Programme

8

Responsibility for module/course

  • I Nyoman Saputra Wahyu Wijaya, S.Kom., M.Cs
  • NIDN : 0826108901

9

Other Information

  1. Pattern Classification (2nd. Edition) by R. O. Duda, P. E. Hart and D. Stork, Wiley 2002,
  2. Pattern Recognition and Machine Learning by C. Bishop, Springer 2006, and
  3. Statistics and the Evaluation of Evidence for Forensic Scientists by C. Aitken and F. Taroni, Wiley, 2004.
  4. Pattern Classification,  R.O.Duda, P.E.Hart and D.G.Stork, John Wiley, 2001.
  5. Statistical pattern Recognition; K. Fukunaga; Academic Press, 2000.
  6. Pattern Recognition, 4th Ed., S.Theodoridis and K.Koutroumbas, Academic Press, 2009.
  7. Machine Learning in Pattern Recognition. Chetanpal Singh. European Journal of Engineering and Technology Research. DOI: 10.24018/ejeng.2023.8.2.3025
  8. Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research. Joel Serey, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Claudia Durán, Rodrigo Ternero, Ricardo Rivera and Jorge Sabattin. Symmetry. https://www.mdpi.com/journal/symmetry. 2023.
  9. Classification in Pattern Recognition: A Review. Priyanka Sharma Manavjeet Kaur. International Journal of Advanced Research in Computer Science and Software Engineering. http://www.ijarcsse.com/ . 2013.
  10. Any scientific articles in Pattern Recognition Letters. https://www.sciencedirect.com/journal/pattern-recognition-letters/issues
  11. Any scientific articles in IEEE Transactions on Pattern Analysis and Machine Intelligence. https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34