This is to track ongoing personal goals. At some point I should add some
historical goals here too.
- No internet – 4 weeks (since 2019-07-20).
(Previous record: about 70 days).
- Writing 3x / week, at least 5 minutes each time – just started (since
- Home study 2x / week, at least 1 hour each time – 4 weeks (since
- Math/CS eclectic reading 3x/week, at least 15 minutes each time – 2
weeks (since 2019-08-03).
I really like making plans for things to study. Sometimes I even finish!
To clarify the difference between this and the reading list:
- reading is me going through a book and (hopefully) remembering some
- studying is me actively putting effort into long term recall. This usually
means homework exercises and exams, but can also be oral exams and presentations, spaced repetition, etc.
- iOS projects
- Rust projects
- Cormen et al, Introduction to Algorithms
- Rice, Mathematical Statistics and Data Analysis
- Bodie, Kane, and Marcus
- Dale Rosenthal (UIUC) slides
- Some kind of practical quant finance / advanced investing
- Programming projects (do it myself!)
- Lopez de Prado
- Dale Rosenthal (UIUC) execution course slides
- Watch out for academic books about modeling (eg Campbell, Cochrane)
Historical / Completed
- MIT 15.501 Introduction to Financial and Managerial Accounting
I bought a bunch of books but didn’t get through most of them.
- AI (Russell–Norvig)
- ML (Murphy, Hastie et al)
- Statistics (Rice, Ch 9-12)
And a brief, aborted stab at Knuth’s Art of Computer Programming (hah!).
This is around when I realized it’s much harder to study while
working full-time and not actively required to. This list is one way to
start fixing things…
Mid-2017 is when I finished my PhD in pure math. Before then all my studying
energy went into getting that done. But afterwards I did get through some things.
- Probability (MIT OCW 6.041)
- Statistics (DeGroot–Schervish, Ch 6-8)
- CS and Finance interview prep (Gayle, Crack)
MIT 15.433 Investments
- This course is too easy for the material. Lots of multiple choice and
simplistic questions. Not enough hard stuff. Not enough programming.
MIT 14.02 Principles of Macroeconomics
- This material is not valuable for my needs. Wrong path for success.
Skeina, Algorithm Design Manual
- Ch 1 complete (2019-08-03)
- The exercises in this book are not exciting enough.
- Better for me: Cormen, MIT, Knuth, Leetcode