Decision theory, parameter estimation, density estimation, non-parametric techniques, supervised learning, linear discriminant functions, clustering, unsupervised learning, artificial neural networks, feature extraction, support vector machines, and pattern recognition applications (e.g., face recognition, fingerprint recognition, automatic target recognition, etc.). 4 hrs. lec. Prerequisites: 36-217, or equivalent introductory probability theory and random variables course and an introductory linear algebra course.
| Popularity index|
| Students also scheduled || |
No comments about this course have been posted, yet. Be the first to post!
Share your opinion on this course with other Pulse readers. Login below or register
to begin posting.