- Instructor: Prof.
- CRN: 23088 & 23167
- Meeting Times: Tuesday 6:00 ~ 8:45 pm, Spring 2007
- Place: PLV G315
- Textbook: Pattern
Classification (2nd. Edition) by Duda, Hart and
Pattern Recognition techniques are useful in many applications of
computer science and information systems, such as information retrieval,
mining, artificial intelligence and image processing. This course is
an introduction to the foundation of pattern recognition
Topics to be studied: data structures for pattern representation,
feature extraction and selection, parametric and non-parametric
classification, supervised and non-supervised learning, clustering,
decision trees, nearest neighbor, artificial neural networks
and hidden Markov models. Applications of
various classification techniques will be demonstrated by several
on-going handwriting, graphics, and speech recognition projects.
- Prerequisites: CS242 Data
Structures and Algorithms II, No previous background
in Pattern Recognition required.
- Lecture Notes: can be accessed using the http://blackboard.pace.edu
Blackboard Login Procedures for Registered Students are available
- Project: click here.
||Ch 2 Bayes Decision Theory & Text categorization |
||Ch 8 Nonmetric Methods (Decision Tree)
||Ch 8 Nonmetric Methods (Nearest Neighbor & Matching)|
||Image Processing, Indexing, & Retrieval|
||Neural Networks & Biometrics|
||Unsupervised Learning & Clustering|
||Random Processes |
||Combining Multiple Classifiers|
||Presentation, Prj rpt
- Assignments (50%):.
- Project (30%): Students are required to implement
one pattern recognition application, e.g., handwriting,
graphics or speech recognition system (Presentation required.)
- Final Report on your project (20%):