Machine learning

πŸ’‘ About

Hello and welcome to the ML! πŸ‘‹ My hope with this page is to provide you with useful resources (including personal notes and solutions) that should hopefully help you to get the most out of this course.

NB: This is my own initiative and thus the most of the provided solutions should NOT be considered as part of the official course. With that being said, I promise to do my best in order to ensure correctness of the provided solutions.

🧰 Important practical information

πŸ” Course materials

My notes to the whole course can be found here. Note that they might need some update since this year’s version of ML is bit different from the last years. (at leas the first few weeks) Solutions with detailed comment for each exercise session can be found below:

Date Lecture Solution
30. 08. 2022 01: Python exercises πŸ”‘
02. 09. 2022 02: Linear regression - intro πŸ”‘
05. 09. 2022 03: Linear regression - inspecting models closely πŸ”‘
08. 09. 2022 04: Linear regression - optional exercises πŸ”‘
13. 09. 2022 05: Linear regression - data splits, reguralization πŸ”‘
16. 09. 2022 06: Logistic regression - introduction πŸ”‘
20. 09. 2022 07: KNN and softmax regression πŸ”‘
23. 09. 2022 08: Decision theory 🀯 πŸ”‘
27. 09. 2022 09: LDA and QDA one feature 🧐 πŸ”‘
30. 09. 2022 10: LDA and QDA multiple features 🀩 πŸ”‘
04. 10. 2022 11: Classification metrics πŸ€“ πŸ”‘
07. 10. 2022 12: Decision trees 🌳 πŸ”‘
11. 10. 2022 13: Ensemble methods intro 🌲🌳🌲🌳🌲 πŸ”‘
14. 10. 2022 14: Ensemble methods continuation 🌲🌳🌲🌳🌲 πŸ”‘
25. 10. 2022 15: Hard margin SVM πŸ˜… πŸ”‘
28. 10. 2022 16: Soft margin SVM 😎 πŸ”‘
01. 11. 2022 17: Gradient descent πŸͺ› πŸ”‘
04. 11. 2022 18: Feed forward neural network - intro 😍 πŸ”‘
08. 11. 2022 19: FFNN implementation from scratch and intro to PyTorch 😍 πŸ”‘
11. 11. 2022 20: CNN theory and simple practical example 🀌 πŸ”‘
15. 11. 2022 21: Naive Bayes 😻 πŸ”‘
22. 11. 2022 23: Clustering πŸ₯³ πŸ”‘