Machine Learning from a matrix algebra viewpoint using MatLab

This course is aiming at provinding students with an enough background to start with machine learning within a matrix algebra framework. Simulations are to be conducted using MatLab as well as some matrix equation typesetting basics in LaTeX will be explained.
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Before starting...
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To get more out of the course, participants are strongly recommended to download and install in their computers the required software and files listed below:

- Install MatLab.
- Full LaTeX package (MikTex, a Tex Editor, ps/dvi viewer, pdf reader): Installers. Tutorial on how to install: video txt
- LaTeX report template (tutorial: zip, template: link)
- Pattern Recognition toolbox for MatLab (prtools)
Full course material: link
(If available, the use of more recent software versions is strongly recommended).

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First lesson - Introduction to machine learning/pattern recogntion
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Video I (Spanish) - Introduction to machine learning (slides)
Video II (Spanish) - Basics on pattern recognition (slides)
- Pattern recognition books:  web    pdf    
- Pattern recognition glossary: web    pdf
- Pattern recognition techniques: pdf
Video III (English) -  Slides - Linear classification and SVM
                                 Slides - SVM solution and multiclass extension
                                 Slides - Non-supervised SVM.   
A Practical Guide to Support Vector Classification: link 
Advanced Machine Learning Practical 3 Solution: Classification (SVM, RVM & AdaBoost): link
- Lagrange Multipliers Tutorial in the Context of Support Vector Machines: link
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Second lesson - Practing ML using MatLab (prtools)
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Third lesson - Matrix algebra for ML
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Video
- Matrix algebra exercises:  pdf    
- Matrix coobook:  pdf
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Fourth lesson - Typesetting in LaTeX 
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Video
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