Sharif University of Technology
School of Electrical
Engineering
Pattern Recognition -
25-617
Fall 2007
- Instructor: Emad Fatemizadeh
- Location: Old EE Building, Half Floor, Room #3
- Lectures: Sunday and Tuesday (13:30-15:00)
- Type: Graduated
- Credits: 3
- Prerequisites: Probability, Statistics and linear Algebra (Preferred)
- Midterm: In Class Agreement
- Middle
:30%
- Final: 30%
- Homework:
15%
- Research
Project: 25%
- Introduction
to basics concepts and definition
- Bayesian
Decision Theory and Discrimination Functions
- Linear
Discrimination Function (Perceptron and SVM)
- Nonlinear
Discrimination Function (MLP, General Function Approximation, RBF, and non
linear SVM)
- Feature
dimension reduction
- Feature
Generation Using Linear Transform (KL, PCA, ICA, DFT, DCT, DST, Haar, and
wavelet)
- Feature
Generation II
- Clustering
Methods
-
Ch. Bishop, Pattern Recognition and Machine Learning, 1st ed., 2006, Springer Verlage.
S. Theodoridis and K. Koutroumbas, Pattern Recognition, 2nd Ed., 2003, Academic Press.
R. Duda, P. Hart, and D. Stock, Pattern Classification, 2000, Wiley.
- K.
Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Ed.
1990, Academic Press.
- IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI),
IEEE Press.
- Pattern
Recognition, (Pergamon-Elsevier)
- Pattern Recognition
Letters ( Elsevier)
- International Journal of
Pattern Recognition and Artificial Intelligence
- Pattern Analysis and Applications
- International Journal of Document Analysis and
Recognition
- IEEE Transactions on Neural Networks
- Neural Computation
- Neural Networks
- Intelligent Data Analysis
All material of this homepage are free to use for academic only purpose. Last updates: 10/16/2011