- Course Code :
DM351
- Level :
Undergraduate
- Course Hours :
3.00
Hours
- Department :
Digital Media Technology
Instructor information :
Area of Study :
•Define knowledge that enhances skills in fundamental area of pattern recognition
•Use and adopt fundamental and advanced mathematics, basic sciences and computer science theories in all development phases of pattern recognition.
•Solve problems using mathematical knowledge through analyzing and interpreting data.
•Comprehend deeply the basic concepts of pattern recognition to be ready for further and continuous learning
For further information :
Introduction; Probability theory: Bayes’ rule; Parameter estimation; Statistical decision making: discriminate functions; measures of classification performance and measures of classification risk; Non-parametric decision making: Adaptive discriminate functions; Minimum squared error discriminate functions; Clustering techniques: Hierarchical clustering, Partitioning clustering; Artificial neural networks Hopfield nets- Other PR systems: Syntactic pattern recognition; Hidden Markov Model based; Application examples.
For further information :
Books:
Course notes :
Course Notes are available with all the slides used in lectures in electronic form on Learning Management System (Moodle)
Recommended books :
Duda and Hart, "Pattern Classification ", Wiley, latest edition
Web Sites :
• IEEE transactions on Pattern Recognition
• IEEE pattern analysis and machine intelligence
• www.ai.com
For further information :