CSC 449 Pattern Recognition
The primary goal of this course is to introduce students to the field of pattern recognition. Topics covered include statistical pattern recognition, machine learning, and neural networks.
A student who successfully completes this course should, at a minimum, be able to:
- know the algorithms for a variety of statistical classifiers and be able to discuss the advantages/disadvantages of each technique
- know the advantages/limitations of a single-layer perceptron network and be able to implement a single-layer perceptron network
- know the advantages/limitations of a multi-layer perceptron network
- be able to discuss the difference between supervised learning and unsupervised learning
- be able to apply neural networks to a variety of pattern recognition problems