Few remarks about this note
* This note contains some practice exercises to get familiar with course material.
* These questions are there just for you to for practice; they do not represent the final exam.
* Don't ask me if it is going to be in the exam.
* It is up to you if you want to work on these questions.
* If you are unable to solve, we will be there to help you.
* I will be updating this every now and then.
* Please email us if you find mistakes.

Book details
* Bishop refers to Bishop's book "Pattern Recognition and Machine Learning" 2006 print.
* KPM refers to Murphy's book "Machine Learning", 2012 print. Check your version, there might be some difference. If you find that there are significant differences, then please bring your book to me and help me update this note. 

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For pre-requisite
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(Added on Nov 6, 2015) 
Bishop: 1.1, 1.2, 1.5, 1.6, 1.8-1.11, 1.36-1.38, 1.40, 2.1, 2.8, 2.20, 2.24-2.28, 2.31, 2.58

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Curse of Dimensionality
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(Added on Nov 6, 2015) 
Bishop: 1.18-1.20

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Linear regression
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(Added on Nov 6, 2015) 
Bishop: 3.2, 3.3
KPM: 7.2-7.6

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Logistic regression
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(Added on Nov 6, 2015) 
Bishop: 4.7, 4.12-4.15, 4.17-4.20 
KPM: 8.6, 8.7

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Kernel Methods
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(Added on Nov 6, 2015) 
Bishop: 6.1, 6.16, 6.5, 6.9

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SVM
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(Added on Nov 6, 2015) 
Bishop: 7.2, 7.4-7.8
KPM: 14.1, 14.2

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K-means, GMM, EM
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(Added on Nov 6, 2015) 
Bishop: 9.1-9.3, 9.6, 9.8-9.12, 9.14, 9.17-9.19