STA 9890 - Handouts and Additional Notes
Supplemental course notes and links to useful external resources will be posted here.
Supplemental Lecture Notes
Week 1 - Course Overview & Introduction to ML (2025-01-28)
Week 2 - Regression I (2025-02-04)
The YouTube channel “3Blue1Brown” makes excellent videos explaining mathematical concepts. A recent entry discusses gradient descent in the context of Neural Networks. At this point in the course, our focus is still on simpler (convex) methods, so not all of this will be directly applicable, but it is still a useful summary and gives helpful background on how gradient methods remain at the heart of all modern ML.
You may also find value in 3Blue1Brown videos on Linear Algebra and on Probability.
Of these, the following are likely to be particularly useful in this course:
- Vectors
- Linear Transformations
- Matrix Multiplication
- Inverses, Rank, and Null Space
- Dot Products and Duality
- Eigenvectors and Eigenvalues
- Bayes’ Theorem
though you don’t need to watch all of these immediately.
In this course, we will apply calculus techniques (mainly differentiation) to functions \(\mathbb{R}^{p} \to \mathbb{R}\). The website matrixcalculus.org/ is helpful for this work.