This page is a compilation of notes from two different courses (CDS503 and CPC251) which I have been teaching since 2018.

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Table of Contents

1. Introduction to Machine Learning

5. Perceptron

7. Naive Bayes

11. Hierarchical Agglomerative Clustering

12. Principal Component Analysis

13. Linear Discriminant Analysis

14. Subset Selection

16. Bagging

17. Boosting

19. Regularization for Neural Network