This course introduces pattern recognition methods and theory discussing topics such as feature extraction, statistical classification, neural networks, fuzzy logic, support vectors, linear discriminant analysis, principal component analysis, clustering, and unsupervised learning.
Students implement algorithms, apply methods to selected problems, and document findings.
Tutorials:
A Geometric Review of Linear Algebra
Tutorial on Principal Component Analysis
Tutorial on Hidden Markov Models
Tutorial on Support Vector Machines
Links:
Python Programming Language -- Official Website
matplotlib: produces publication-quality figures in Python. See this gallery for some very nice examples.
DISLIN Scientific Plotting Software
Numerical Python Basics -- O'Reilly Media
The MathWorks -- MATLAB and Simulink for Technical Computing
Statistical Pattern Recognition Toolbox for MATLAB
Software:
MATLAB via Tealware with information here.
Papers:
Paper on Automatic Representation of Adult Aging in Facial Images