- Instructor: Prof.
Sung-Hyuk Cha
- Email: scha@pace.edu
- Tel: (212) 346-1253
- Office: 163 William St. 2nd floor rm 234
- Office Hours: T 10am - noon and R 2:30pm - 5:30 pm
- CRN: 23199 & 23200
- 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, generic algorithm,
and hidden Markov models. Applications of
various classification techniques will be demonstrated by several
on-going handwriting, biometrics, and other projects.
- Lectures: will be on the http://blackboard.pace.edu
Blackboard Login Procedures for Registered Students are available
here
- Schedule: (tentative)
| Week |
Reading assignments |
| 1 (1/27) |
Introduction |
| 2 (2/3) |
Nearest Neighbor & Matching |
| 3 (2/10) |
Neural Networks
|
| 4 (2/17) |
Biometrics
|
| 5 (2/24) |
Genetic Algorithms & Decision trees |
| 6 (3/3) |
Image Analysis |
| 7 (3/10) |
Clustering |
| 8 (3/17) |
Spring Break |
| 9 (3/24) |
Clustering Initial report |
| 10 (3/31) |
Numerical Taxonomy |
| 11 (4/7) |
Bayes Decision Theory & Text categorization |
| 12 (4/14) |
Statistics & ROC curves |
| 13 (4/21) |
Random Processes |
| 14 (4/28) |
Lab |
| 15 (5/5) |
Studyday? Final Presentation |
| 16 (5/12) |
Final Presentation |
- Evaluation:
- Participation (25%):
- Homeworks (25%):
- Research Project (50%): Two reports & Final presentation.
|