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Statistical Pattern Recognition

Statistical Pattern Recognition

2nd Semester 98-99 (Jan. 2020)

 

Class Meetings: Sat. and Mon. from 08:00 am to 09:30 am

Room #5-Aboureyhan Build.

Instructor: Dr. Mohammad Reza Ahmadzadeh

Office Location: ECE Building - 212B

Internal Telephone: Ext. 5370

External Telephone: Iran: +98 (0)31 33915370

Fax: Iran: +98 (0)31 33912451

Email: Ahmadzadeh @ iut.ac.ir

Homepage: https://ahmadzadeh.iut.ac.ir/

 

Office Hours:

I will try to be in my office on Saturdays to Tuesdays from 09:30 to 10:00 am, but I will always do my best to be available for students by appointment or any time that I am free.

Course Description:

This course covers the fundamentals of Pattern Recognition techniques, both supervised and unsupervised learning algorithms. Machine intelligence algorithms to be presented include parametric and non-parametric pattern detection and classification, logistic discrimination, support vector machines, decision trees, feature extraction and selection, principal component analysis, independent component analysis, clustering, artificial neural networks, and others.

Outline:

1- Introduction

2- Bayesian decision theory

3- Maximum likelihood and Bayesian parameter estimation

4- Nonparametric techniques

5- Linear Discriminant Functions

6- Nonlinear Classifiers

7- Feature Selection

8- Algorithm-independent machine learning

9- Unsupervised Learning and Clustering


Textbook:

1- Pattern Recognition, 4th Ed., Theodoridis and Koutroumbas

2- Duda, Hart and Stork, Pattern Classification, Second Edition, Wiley, 2001.

The textbook has a web site. In particular, you may find the errata list useful.

Slides from the authors of the book.

Optional:

1- Bishop, Christopher M.  Pattern Recognition and Machine Learning, 2006

 URL: http://research.microsoft.com/~cmbishop/PRML/index.htm

2- Andrew R.Webb • Keith D. Copsey, Statistical Pattern Recognition, 3rd Ed., 2011.

 

Lecture Notes:

Old  video Lectures (2014) - Google Drive

Slides modified by Instructor and lecture notes can be obtained via E-Learning LMS (Enrolled students, Password protected).

Link: http://lms.iut.ac.ir/

Prerequisites: 

Probability and Stochastic Processes for Engineers or equivalent.

Grading Policy: 
Midterm Exam ≈25%±5%
HW, Comp. Assignments and projects: ≈ 30%
Final exam ≈ 45%±5%

 

Time: 

Sat. and Mon. from 08:00 am to 09:30 am

Term: 
2020
Grade: 
Graduate

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