Machine Learning and Deep Learning

This page features a comprehensive compilation of notes from two courses (CDS503 and CPC251) that I have been teaching since 2018. I have compiled and expanded these notes into a complete textbook. You can download the book here.

For further reading, please explore my journal article (survey) on Deep Learning Applications and Challenges.

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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

9. Decision Tree

10. Support Vector Machine

11. k-Means Clustering

12. Hierarchical Agglomerative Clustering

13. Principal Component Analysis

14. Linear Discriminant Analysis

15. Subset Selection

16. Ensemble Learning

17. Bagging

18. Boosting

19. Artificial Neural Network

20. Regularization for Neural Network

21. Convolutional Neural Network

22. Recurrent Neural Network

23. Attention Mechanism