CSCI 410 - Pattern Recognition

3 Credits
This course will cover the different types of data classification and how they are applied to the algorithms that classify the unique biometric traits. Supervised and unsupervised learning methods will be covered. Linear models for regression/classification, nearest-neighbor, and neural networks are just some of the topics that may be covered.

Fees: Additional course fees apply.
Prerequisite(s): CSCI 222 and CSCI 312
Course Learning Outcomes:
1. Implement algorithms for statistical pattern recognition.
2. Apply Bayesian decision theory.
3. Critique the pattern recognition algorithms currently being used in industry and research.
4. Evaluate recent advances in pattern recognition.