Engineering 252

Text: Pattern Classification (2nd ed.) by R.O. Duda, P.E. Hart, D.G. Stork

Plus some application material on Computer Vision for illustrating the

theory and methodology.

Grade Basis:

1. Midterm Examination
2. Final Examination
3. Two MATLAB Projects

Homework Problems: 7 assignments

1. Pattern representation by features and stochastic processes.
2. Bayesian classification theory.
3. Supervised learning: parametric estimation and recognition.
4. Supervised learning: nonparametric estimation and learning 
(e.g. nearest neighbor methods).
5. Unsupervised learning: parametric estimation and recognition.
6. Unsupervised learning, nonparametric: clustering and recognition.
7. Artificial neural networks.
8. Learning theory, complexity, and high dimensional feature spaces.

Computer Vision Applications To:

1. Texture recognition.
2. Stereo reconstruction of 3D surfaces.
3. 2D and 3D shape modelling and recognition using algrebraic curves and surfaces.

Prerequisite:

A senior level undergraduate course in statistics or probability. Some linear algebra.