Description

In-depth coverage of the fundamentals of pattern recognition and knowledge representation with examples. Topics include: decision functions (linear decision functions, generalized decision functions), pattern classification by distance functions (minimum distance pattern classification, Cluster seeking), pattern classification by likelihood functions (Bayes classifier), structural pattern representation (grammars for pattern representation, picture description language and grammars, stochastic grammars), structural pattern recognition (String to string distance) and matching other structures (relational structures, graph matching, matching by relaxation, random graph).

Prerequisites:

• CSC 305 + Lab : Object-Oriented Programming

• MAT 203 : Linear Algebra: Vectors and Matrices

• MAT 212 : Probability & Statistics for Science and Engineering

Expected Outcome(s):

• Design systems and algorithms for pattern recognition (signal classification), with focus on sequences of patterns that are analyzed using, e.g., hidden Markov models (HMM).
• Analyze classification problems probabilistically and estimate classifier performance.
• Understand and analyze methods for automatic training of classification systems.
• Apply Maximum-likelihood parameter estimation in relatively complex probabilistic models, such as mixture density models and hidden Markov models.
• Understand the principles of Bayesian parameter estimation and apply them in relatively simple probabilistic models.

Suggested Books:

1. Pattern Recognition and Machine Learning by Christopher M. Bishop
2. Pattern Classification by Richard O. Duda, Peter E. Hart, David G. Stork

Grading Policy:

Biweekly Quiz, One Midterm Exam, One Final Exam, Project

Letter Grade	Marks	Grade Point
A	90 - 100	4.00
A-	85 - 89	3.70
B+	80 - 84	3.30
B	75 - 79	3.00
B-	70 - 74	2.70
C+	65 - 69	2.30
C	60 - 64	2.00
C-	55 - 59	1.70
D+	50 - 54	1.30
D	45 - 49	1.00
F	00 - 44	0.00

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