- Instructor: Prof.
Sung-Hyuk Cha
- CRN: 51114
- Meeting:
- Meeting Times: F 09:05AM~01:15PM, Fall 2004
- Place: TBA
- Textbook: Pattern
Classification (2nd. Edition) by Duda, Hart and
Stork
- Description:
Pattern Recognition techniques are useful in many applications of
computer science and information systems, such as information retrieval,
data
mining, artificial intelligence and image processing. This course is
an introduction to the foundation of pattern recognition
algorithms.
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
here
- Project: click here.
- Schedule:
| Week |
Topic |
| 1 (9/10) |
Introduction |
| 2 (9/17) |
Ch 2 Bayes Decision Theory & Text categorization |
| 3 (9/24) |
Ch 8 Nonmetric Methods (Decision Tree)
|
| 4 (10/1) |
Ch 8 Nonmetric Methods (Nearest Neighbor & Matching)
Signature Verification, Prj Proposal
due
|
| 5 (10/8) |
Image Processing, Indexing, & Retrieval |
| 6 (10/15) |
Image Understanding |
| 7 (10/22) |
Speech Understanding by Dr. Tappert |
| 8 (10/29) |
Speech Understanding by Dr. Tappert |
| 9 (11/5) |
Artificial Neural Networks |
| 10 (11/7) |
Biometric Authentication & ANN |
| 11 (11/14) |
Ch 7 Evolutionary Methods (GA & GP) |
| 12 (11/21) |
Ch 10 Unsupervised Learning & Clustering |
| 13 (11/28) |
Thanx giving break |
| 14 (12/5) |
Open |
| 15 (12/12) |
Presentation, Prj rpt
due |
- Evaluation:
- Homeworks (50%):There will be 8 homeworks.
- 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%):
- Student
Responsibilities
|