Lecture Notes and Slides
The following hot links allow you to retrieve lecture notes (in PDF format). Links will fail if notes have not yet been posted.
These lecture notes are in a constant state of flux. I often update them after a lecture to add extra material and to correct errors. A class note will not be the final state until after I have finished with the particular topic. So come back and check for update often.
"Happy Face Notes" the ones that I intend to cover. "Sad Face Notes" the ones most likely will not be covered.
Supervised techniques
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Introduction to Pattern Recognition (PDF
format) -
Parameter Estimation Techniques (PDF
format)
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Non-parametric Estimation Techniques (PDF
format)
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Kalman Filter and Particle Filter (PDF
format) -
Linear Discriminant Functions (PDF
format)
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Linear and Logistic Regression (PDF
format)
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Support Vector Machines (PDF
format) (PDF
format, by Nello Chrisianini, BIOWulf Technlogy)
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Dimension Reduction Techniques (PDF
format)
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Decision Trees (PDF
format)
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Generalization Performance (PDF
format)
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Introduction to Artificial Neural Networks (PDF
format)
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Simple Perceptrons (PDF
format)
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Multi-layer Perceptrons (PDF
format)
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Deep Learning for Computer Vision (PDF
format)
Advanced topics in supervised techniques
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Kernel Methods (PDF format)
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Quadratic Program (PDF format) (QP is used as the workhorse numerical method
in Kernel Methods and SVM. Warning: refresh your linear algebra before proceed)
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Radial Basis Function Networks (PDF
format)
Unsupervised techniques
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Un-supervised Clustering & Learning with Mixture Densities (PDF
format)
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Un-supervised Clustering (PDF
format)
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Un-supervised Learning (PDF
format)
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Un-supervised Clustering & Indepdent Component Analysis (PDF
format)
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Un-supervised Data Mining (PDF
format)
Other topics (to be covered if time permits)
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Memory Models(PDF
format)
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Correlated and Time-Varying Events (PDF
format)
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Reinforcement Learning (PDF
format)
Notes are in PDF format. You need the Acrobat Reader to view them. You can
download the Acrobat Reader for free here 
Important Note: The notes contain many figures and graphs in the book “Pattern Recognition” by Duda, Hart, and Stork. The use is permitted for this particular course, but not for any other lecture or commercial use. Hence, I cannot grant permission of copying or duplicating these notes nor can I release the Powerpoint source files.
