All supplementary material used by me while TA-ing CS3244: Machine Learning at NUS School of Computing.

I teach TG-06, the tutorial that takes place every Monday, 1200-1300 in AY21/22 Semester 1. It is fully online this semester.

Unless the syllabus has drastically changed, I believe the material covered here is relevant for future AYs as well (eg: AY22/23++). The module might be deprecated soon so do take note! In future iterations of SOC's Intro to ML module, I still feel the material herein is good enough for preparation purposes.

This repository contains code, figures, and miscelleaneous items that aid me in teaching my class. The main source of reference should be the lecture notes and tutorial questions created by the CS3244 Professors and Teaching Staff.

Official tutorial solutions will be released at the end of every week.

Here's a list of what I've covered / I'll be covering in my tutorials:

• T1W3: k-Nearest Neighbours
• T2W4: Decision Trees
• T3W5: Linear Models
• T4aW6: Bias-Variance Tradeoff
• T4bW7: Regularisation & Validation
• T5W8: Evaluation Metrics
• T6W9: Visualisation & Dimensionality Reduction (Approach TA Pranavan)
• T7W10: Perceptrons and Neural Networks
• T8W11: CNNs and RNNs
• T9W12: Explainable AI
• T10W13: Unsupervised Learning

The link to the slides for all my tutorials can be found in the README.md in each week's respective folder.

I've prepared some extra resources that might aid you in your exam preparation. You can find the files here:

• Midterm Cheatsheet: Lectures 1a: Intro & Class Org. to 6: Bias Variance Tradeoff

If there are any issues or suggestions, feel free to raise an Issue or PR. All meaningful contributions welcome!

MIT