This repository contains all the material that I've used to study for the Machine Learning course in the MSc in AI @ UniBo (2022/2023).
It contains a series of complete and comprehensive notes on ML, which explain the main techniques in Classification, Clustering, Association rules, etc.
Additionally, it contains some solutions to the exercises of the previous exams, as well as some code templates to use during the exam.
I believe that the material in here can be very useful to anyone, not just students of this course.
For the first time ever, the notes are all written in pure English.
The notes in here are taken lesson by lesson, and are basically a transcript of everything the professor has said during the course. I firmly believe that the notes in here can be used as a way to fully study this course.
There are many features that may ease the process of learning this topics, such as:
- Syntax highlighting for important words and concepts.
- Transcription of additional useful information told during the lessons.
- Explanations of images.
- Shortcuts to previously mentioned topics (they can only be used properly inside Obsidian though).
- And much more...
All the notes are written in Obsidian Markdown, and can be found in the Notes/ folder. A PDF version can also be found.
To read this properly inside Obsidian, you'll need to import this repository as a vault, which is pretty easy to do.
I think this notes are best read on a laptop or tablet, directly using the Obsidian app.
- Write all notes
- Render notes in PDF
- Add Lab solutions
- Add solutions to (some) past exams
- Add templates
You can open a PR to:
- Correct any errors in the notes.
- Correct any errors in the code or add comments.
- Complete any of the unfinished tasks.
Alternatively, you can write an email to the address in my profile page.
If you find these notes useful, please consider starring these repository 😄.
[This repository is also known as "Angelino's Ultimate Machine Learning Guide" by friends]