ILOs

Intended Learning Outcomes (ILOs) of the Pattern Recognition (PR) course: After passing the course, students should be able to:

• Explain about the task of pattern recognition - deciding whether different patterns exist in observed signals and are they worth to recognize.
• Describe a general model for pattern recognition system and formulate optimal cost functions.
• Formulate and analyze pattern recognition problem in a probabilistic framework, and estimate recognizer performance.
• Describe a pattern recognition problem for a sequence of observed signals and address the problem using hidden Markov model (HMM).
• Design systems and algorithms for pattern recognition. Critically compare the algorithms in a trade-off between complexity and performance. Finally report the results.
• Understand, implement and analyze methods for automatic training of pattern recognition system.
• Understand principles of Bayesian learning methods and apply them for relevant problems.

 The above ILOs are improvement over the previous ILOs in the course.