EE5907R
PATTERN RECOGNITION (2015/2016, Semester 1) 

 MODULE OUTLINE Created: 06-Jul-2015, Updated: 06-Jul-2015
 
Module Code EE5907R
Module Title PATTERN RECOGNITION
Semester Semester 1, 2015/2016
Modular Credits 4
Faculty Engineering
Department Electrical & Computer Engineering
Timetable Timetable/Teaching Staff
Module Facilitators
DR Yeo Boon Thye Thomas Coordinator
ASSOC PROF Yan Shuicheng Coordinator
KONG RU Teaching Assistant
DR NGUYEN VAN TAM Co-Lecturer
Weblinks
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Learning Outcomes | Prerequisites | Assessment | Preclusions | Workload | References


 LEARNING OUTCOMES Top
Pattern recognition deals with automated classification, identification, and/or characterisations of signals/data from various sources. The main objectives of this graduate module are to equip students with knowledge of common statistical pattern recognition (PR) algorithms and techniques. Course will contain project-based work involving use of PR algorithms. Upon completion of this module, students will be able to analyse a given pattern recognition problem, and determine which standard technique is applicable, or be able to modify existing algorithms to engineer new algorithms to solve the problem. Topics covered include: Decision theory, Parameter estimation, Density estimation, Non-parametric techniques, Supervised learning, Dimensionality reduction, Linear discriminant functions, Clustering, Unsupervised learning, Feature extraction and Applications.


 PREREQUISITES Top
EE2012 and CS1101C (Applicable to undergraduate students only)


 ASSESSMENT Top
CA (40%): 2 mini-projects (2 x 20%).
Final Exam (60%): close book; one A4 size formula sheet is allowed.


 PRECLUSIONS Top
TD5133


 WORKLOAD Top
2.75-0-0-2-5.25

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week


 
 2. TEXT & READINGS Top
 
Optional textbooks:

1) Pattern Classification by Duda, Hart and Stork, John Wiley, 2001

2) Machine Learning: A Probabilistic Perspective, Kevin Murphy, 2012

3) C
omputer Vision: Models, learning and inference, Simon Prince, 2012 (free download: www.computervisionmodels.com)

 


Learning Outcomes | Prerequisites | Assessment | Preclusions | Workload | References