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
|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|
|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|
|Neural Networks – Clustering L1||00:25:00|
|Hierarchical Clustering L2||00:10:00|
|Applications in Medicine||00:40:00|
|Submit Your Assignment||00:00:00|
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