Semester: Fall 2005
Lecture: 3 points, MW 1:10-2:25 pm
Location: Mudd 1024
Many emerging applications, such as indexing, security, forensics, and information discovery, involve the use of novel ideas and effective techniques in teaching computers to recognize patterns in various signals and data, ranging from documents, images, audio, and other sensory signals.
This course will include introduction to basic theories, algorithms, and practical solutions of statistical pattern recognition. Topics covered include feature extraction, feature selection, Bayesian classifiers, neural networks, discriminative classifiers, clustering, performance evaluation, and fusion of models.
Students will gain understanding of algorithm design, mathematical tools, and practical implementations of various applications, with special emphasis on image classification and multimedia indexing. Benchmark datasets, such as TRECVID news video, and evaluation metrics will be used for homeworks and the course project.