Machine Learning and Deep Learning

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

2. Supervised Learning

3. Linear Regression

4. Gradient Descent

5. Perceptron

6. Logistic Regression

7. Naive Bayes

8. k-Nearest Neighbors

8. Decision Tree

9. Support Vector Machine

10. k-Means Clustering

11. Hierarchical Agglomerative Clustering

12. Principal Component Analysis

13. Linear Discriminant Analysis

14. Subset Selection

15. Ensemble Learning

16. Bagging

17. Boosting

18. Artificial Neural Network

19. Regularization for Neural Network

20. Convolutional Neural Network

21. Recurrent Neural Network

22. Attention Mechanism