Additional materials created by TAs, students and instructor.
We already have a few interesting animations made by Anirban:
- Effect of noise on Regression
- How KNN selects nearest neighbors and why it produces jumps?
Further Reading on Regression:
- https://blog.quantinsti.com/linear-regression-assumptions-limitations/
- https://www.rdatagen.net/post/linear-regression-models-assume-noise-is-independent/
- https://www.statisticshowto.com/experimental-design/confounding-variable/
We have simiplified versions of notebooks for chapters of Hands on ML book. They follow the following schedule. You can practice them on your own or during tutorial hours and seek help from the TAs if needed.
- Week of Aug 22: Ch 3, Ch 5 [Classification, SVM]
- Week of Aug 29: Ch 6, Ch 7 [Decision Trees, Ensemble]
Theory Materials:
- Linear Algebra: Needed for Regression. You can study Linear Algebra, 5th edition by Gilbert Strang.
- Calculus and Optimization: Needed for Gradient descent, Lagrangian Methods for SVM. You can study
- Probability and Statistics: Needed for Distributions, Confidence Intervals. You can study Probability and Statistics for Engineers by Sheldon Ross.
Students can submit PR for their work here for the suggested practice exercises in Class. This will count towads bonus marks.