CSci 5521 Fall 2024 - - Notes

Supplementary Material on Textbook

• Textbook lecture notes - - Lecture notes by Ethem Alpaydin, based on 3rd edition (author of text book, from author's own web site)

Background Material

• There are many entire books on linear algebra. Some of the most readable are by Gilbert Strang (there is an introductory level book and a more advanced one). A short review of the basic theory from a computational/numerical point of view can be found in first part of chapters 2,3,4 of Scientific Computing by Michael Heath, especially chapter 3. See electronic copy of Michael Heath book (available via VPN to the U - you must use 'full tunnel', not 'split tunnel')
• Some lectures on linear algebra from MIT.
• Some lectures on probability and statistics from MIT.
• A brief review of linear algebra.
• A Matrix Cookbook.

Supplementary Material

• Topics for the First Test
• Discriminant Function: Bayes Analysis with Gaussian Distribution
• Bayes Net Example
• 2D Taylor Expansion, Partial Derivatives and Gradients.
• Principal Component using the SVD.
• EM.pdf Expectation-Maximization process.

Outside References

• Pattern Recognition and Machine Learning by Christopher M. Bishop A very thorough intense treatment of machine learning.
• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville an entry level book on deep learning neural networks.
• A Beginner's Guide To Understanding Convolutional Neural Networks
• An Intuitive Explanation of Convolutional Neural Networks
• Demo of Convolutional Neural Networks

Python Hints

• Python Hints including some hints on installation.

Matlab vs R vs Python

• NumPy for Matlab users
  Table of Rough Matlab-Numpy Equivalents
• MATLAB / R Reference by David Hiebeler
• R for MATLAB users
• R for OCTAVE users
• Comparison Chart of Matlab vs python numpy package (old)
• Chart of Matlab vs R vs Python numpy package

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