Learning about Learning - Part 1
The plan for the next few weeks is to develop a deep understanding about machine learning and statistics. Even though I have used and applied machine learning to many projects, and have taken a number of offline and online courses on machine learning, I feel like I don’t have a visceral understanding of it and have barely scratched the surface. My goal is to have a strong theoretical foundation and avoid the “throw things at it and see what sticks” approach.
Stage 1: Build a strong mathematical foundation, be able to reason about probability from a measure theoretic perspective and fill in holes in math. The plan of attack is:
- Learn real analysis by going through Francis Su’s Youtube lectures and using the following books as reference
- Principles of Mathematical Analysis by Rudin
- Understanding Analysis by Abbott
- Real Mathematical Analysis by Pugh
- The next step is to do some