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The target of this course is to new elective course is first to make student familiar with general approaches such as bayes classification, nearest neighbor Rule, neural Networks. Pattern Recognition (PR) techniques are widely used for medical and biological applications. The Invited speakers will share their thesis work. By this discussion students will get to know about all the information about this course.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: Middle East Technical University

Course Curriculum

Module: 1
Introduction to Pattern Classification. Definitions L1 00:05:00
Data, Measurement, Features L2 00:20:00
MATLAB for Pattern Recognition 00:30:00
Statistical Pattern Classification L1 00:25:00
Bayesian Belief Networks L2 00:07:00
Maximum Likelihood Parameter Estimation 00:45:00
Module: 2
Nearest and k-nearest Neighbor Classification L1 00:10:00
Density Estimation L2 00:05:00
Linear Discriminant Functions 00:20:00
Support Vector Machines 00:30:00
Neural Networks as a tool for Pattern Classification. Multilayer Perceptron and Back-propagation 00:20:00
Review 00:10:00
Module: 3
Neural Networks – Clustering L1 00:25:00
Hierarchical Clustering L2 00:10:00
Performance Analysis 00:40:00
Applications in Medicine 00:40:00
Homeworks L1 00:05:00
Homeworks L2 00:05:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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