CS855 Pattern Recognition and Machine Learning

Dr. Chuck Tappert, Room 325, Goldstein Academic Center, Pace University, Pleasantville.

Graduate Assistant: Vinnie Monaco   Lobby Security: 4166

Class Meetings:
Thursdays, 1-4pm, Room 311, Pace Graduate Center, 1 Martine Ave, White Plains, across street from the White Plains railroad station, half hour from Grand Central Station in NYC.

Textbooks and Additional Information:
Pattern Classification, 2nd Edition, Duda, Hart, and Stork. Wiley 2000. Errata for early printings
Additional technical material will be provided (journal articles, conference papers, book sections).

Other Textbooks Available Online (useful as aditional reading, not required):
Pattern Recognition and Machine Learning, Bishop. Springer 2006.
The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman. Springer 2011.
Data Mining (3rd Ed.), Witten, Frank, and Hall. Morgan Kaufmann 2011.
Pattern Recognition Resources (this Duda book not same as print version).

Course Description:
This course focuses on the fundamental concepts, theories, and algorithms for pattern recognition and machine learning. Diverse application areas such as optical character recognition, speech recognition, and biometrics are discussed. Topics covered include supervised and unsupervised (clustering) pattern classification algorithms, parametric and non-parametric supervised learning techniques, including Bayesian decision theory, discriminant functions, neural networks, and the nearest neighbor algorithm.

Learning Outcomes and Major Topics:

Evaluation for Course Grade: