Machine Learning and Data Mining, Winter Semester 2024-2025

Set up an environment for working with Jupyter Notebooks

Please follow the instructions in one of the following two folders:

1. GuideForVisualStudioCode - for working in a local development environment (recommended by Simo).
2. GuideForGoogleColab - for working using a remote Google Cloud session.

Sessions with Simo

1. ✅ 07.10, Monday, 8 am, 013
2. ✅ 09.10, Wednesday, 5 pm, 013
3. ✅ 14.10, Monday, 8 am, 013
4. ✅ 21.10, Monday, 8 am, 013
5. ✅ 28.10, Monday, 8 am, 013
6. ✅ 04.11, Monday, 8 am, 013
7. ✅ 11.11, Monday, 8 am, 013
8. ✅ 18.11, Monday, 8 am, 013
9. ✅ 27.11, Wednesday, 5 pm, 013
10. ✅ 02.12, Monday, 8 am, 013
11. ✅ 11.12, Wednesday, 5 pm, 013
12. ✅ 16.12, Monday, 8 am, 013
13. ✅ 17.12, Tuesday, 11 am, 013
14. 06.01, Monday, 8 am, 013
15. 13.01, Monday, 8 am, 013

Schedules

• Homework 1: 15/16.11 - 08.12.
• Homework 2: 14/15.12 - 12.01.
• Projects: 16/22.12 - date of defense.

Resources

Python

• Jupyter Notebooks in 30 minutes
• Python in 2 hours
• Python in 4 hours (recommended by Simo)
• Python in 5 hours
• Python in 14 hours

Markdown

• Markdown Cheat Sheet – How to Write in Markdown with Examples
• Markdown Cheatsheet

Mathematics

• The essence of linear algebra
• The essence of calculus
• Statistics (recommended by Simo)
• Algebra 1 Final Exam Review
• Trigonometry Final Exam Review
• Calculus 1 Final Exam Review
• Calculus 2 Final Exam Review
• Physics 1 Final Exam Review
• Physics 1B Final Exam Review
• Physics 2 Final Exam Review

Machine Learning and Deep Learning

• Machine Learning Playlist
• Deep Learning with Tensorflow and Keras
• The Guru of Machine Learning teaching Machine Learning!
• But what are neural networks?

Books

• If you are part of the Moodle class, you should have received more information about recommended books by Simo at the start of the semester.
• If you are part of the Moodle class, but haven't received anything, feel free to contact Simo via his email address.

Don't be afraid to ask questions 😊

He who asks a question is a fool for five minutes; he who does not ask a question remains a fool forever.

-- Chinese Proverb

Principles of this course

1. You learn as much as you want.
2. Be proactive.
3. Using generative AI is discouraged. Instead, try to research via Google on your own and read documentations of various libraries. You can also ask me, of course.
4. Using generative AI to solve the tasks, given in this course, is strongly discouraged.
5. We may not have enough time to go through all the planned material for each session. In such cases, please read the remainder on your own.
6. You'll get the solution of a task if you first try to solve it yourself.

Frequently asked questions

How will I be graded?

There will be two homeworks, a project and a discussion. No exams are planned.

What is "the discussion"?

• The discussion is done after the defense of the project on the date of the final exam.
• It forms 50% of the final grade.
• If I'm available to tune in for the final, I'll be asking questions (some will be in an interview style) regarding the topics I've covered.
• Example questions:
  • Tell me everything you know about boosting?
  • How can we deal with missing values?
  • What's an important step to do when splitting data for a classification problem?
• The complexity of the questions will be inversely proportional to the number of submitted "For Home" weeks. More submissions => easier questions (from my side).
  • I'll ask no questions if all "For Home" weeks are submitted.

Who has more information about the theory exam?

There will be no theory exam(s).

When will project topics be provided?

Two weeks before the end of the semester.

Can I submit my own project idea?

Yes. I'll let you know when there's a submission form for that. Note that in order for you to start working on your idea, it has to be approved by us.