Course Code: KIE4025
Course Title: Pattern Recognition
Credit Hour: 2
Course Description: Pattern recognition techniques are used to design automated systems that improve their own performance through experience. This course covers the methodologies, technologies, and algorithms of statistical pattern recognition from a variety of perspectives. Topics including Bayesian Decision Theory, Estimation Theory, Linear Discrimination Functions, Nonparametric Techniques, Support Vector Machines, Neural Networks, Decision Trees, and Clustering Algorithms etc. will be presented.
|
COURSE LEARNING OUTCOMES (CLO) |
PROGRAMME LEARNING OUTCOMES (PLO) |
METHOD OF ASSESSMENT & ASSESSMENT WEIGHTAGE |
||
| PLO3 | PLO4 | PLO5 | ||
| 1. Explain basic concepts in pattern recognition and state of the art algorithm used in pattern recognition research. | ✔ | Test (10%) and final examination (20%) | ||
| 2. Evaluate pattern recognition theories, such as Bayes classifier, linear discriminant analysis using suitable tools/method of assessment. | ✔ | Test (10%) and final examination (20%) | ||
| 3. Decide pattern recognition techniques in practical problems. | ✔ | Assignment (20%) and final examination (20%) | ||
Course resources will be made available on the SPeCTRUM site.
Last Update: 22/09/2021