This document serves as an educational resource primarily designed for the courses I teach, including:

• CSE 474/574 Introduction to Machine Learning
• CSE 455/555 Pattern Recognition
• CSE 676 Deep Learning

Additionally, it functions as a personal reference in the field of machine learning.

This compilation aims to provide an extensive overview and guide for students and practitioners of machine learning, incorporating a blend of direct references and adaptations from established texts and sources. It is intended for:

• Educational Aid: As a supplementary resource for teaching and learning.
• Reference Material: For personal and academic use.

This document is not authorized for commercial use, redistribution, or sale without explicit consent. To read the pdf version of the document: PDF

From my side there will be constant updates but in order to motivate my students to read I have a 3 bonus points if they make significant and meaningful contribution to each chapters of the note. I welcome contributions and feedback from the community! If you have suggestions, corrections, or additional material you think would enhance this resource, please feel free to contribute. Here's how you can do that:

• Fork the Repository: Create your own fork of the project.
• Make Changes: Add your contributions or modifications.
• Submit a Pull Request: Open a pull request to the original repository with a clear list of what you've done.
• Review & Merge: I will review your changes and merge them into the main document as appropriate.

Details on how to create a pull request: Creating a pull request

When contributing, please adhere to the following guidelines:

• Ensure that any added content is accurate and relevant to machine learning.
• Respect intellectual property and cite sources appropriately.
• Maintain a respectful and constructive tone in discussions and pull requests.

LaTeX is a high-quality typesetting system; it includes features designed for the production of technical and scientific documentation. My preferred setup for writing LaTeX documents is Visual Studio Code (VSCode) with the LaTeX Workshop extension, offering a user-friendly and efficient LaTeX writing experience. Below is a guide to help you set up this environment.

To begin with, you need a LaTeX distribution installed on your computer.

• Windows: Use MiKTeX or TeX Live. MiKTeX is more user-friendly for beginners, while TeX Live is more comprehensive. Download from MiKTeX or TeX Live.
• macOS: Install MacTeX, which is a macOS version of TeX Live with additional tools. Download from MacTeX.
• Linux: Most Linux distributions include TeX Live in their package repositories. Install it using your package manager (for example, sudo apt-get install texlive-full in Ubuntu).

VSCode is a free, open-source editor with a wide array of features.

• Download and install VSCode from the official website.

LaTeX Workshop enhances VSCode with LaTeX typesetting capabilities.

• Open VSCode.
• Navigate to Extensions (Ctrl+Shift+X / Cmd+Shift+X).
• Search for "LaTeX Workshop" and install the extension.

Customize LaTeX Workshop settings for your needs.

• Go to File > Preferences > Settings (Code > Preferences > Settings on macOS).
• Search for "LaTeX Workshop" settings.
• Configure according to your preferences, like setting up a default compiler or enabling auto-build on save.

Now, you are ready to write LaTeX documents.

• Create a new file with a .tex extension in VSCode.
• Write your LaTeX content.
• Use the build feature in LaTeX Workshop to compile your document into a PDF.

• Git Integration: VSCode's integrated support for Git is beneficial for version controlling your LaTeX documents.
• Live Preview: LaTeX Workshop supports live preview of your document.
• Custom Snippets: Create custom snippets for frequently used LaTeX commands to improve efficiency.

This setup with VSCode and LaTeX Workshop provides a powerful, modern environment for writing and managing LaTeX documents, blending LaTeX's typesetting capabilities with the features of a contemporary code editor.

The majority of the material referenced in this document comes from the following key sources:

1. Zhang, Aston, et al. "Dive into Deep Learning." Cambridge University Press, 2023.
2. Bishop, C. M., & Nasrabadi, N. M. "Pattern Recognition and Machine Learning" (Vol. 4, No. 4, p. 738). New York: Springer, 2006.
3. Hart, P. E., Stork, D. G., & Duda, R. O. "Pattern Classification." Hoboken: Wiley, 2000.
4. Burkov, A. "The Hundred-Page Machine Learning Book" (Vol. 1, p. 32). Quebec City, QC, Canada: Andriy Burkov, 2019.
5. Burkov, A. "Machine Learning Engineering" (Vol. 1). Montreal, QC, Canada: True Positive Incorporated, 2020.

All other referenced materials and sources are cited in the bibliography section of this document.

For inquiries, permissions, or further information, please reach out to me at ( [email protected] ).

This document is provided "as is," and the author makes no representations or warranties, express or implied, regarding its completeness, accuracy, or reliability.