Goals

This is to track ongoing personal goals. At some point I should add some historical goals here too.

Practices (see Results vs Practices

• 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 2019-08-17).
• Home study 2x / week, at least 1 hour each time – 4 weeks (since 2019-07-20).
• Math/CS eclectic reading 3x/week, at least 15 minutes each time – 2 weeks (since 2019-08-03).

Study Plans

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 content.
• 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.

CS

• iOS projects
• Rust projects
• Cormen et al, Introduction to Algorithms
  • MIT 6.006 => 6.046

Math

• Rice, Mathematical Statistics and Data Analysis
  • MIT 18.650

Finance

• Investing
  • Bodie, Kane, and Marcus
  • Dale Rosenthal (UIUC) slides
• Some kind of practical quant finance / advanced investing
  • Programming projects (do it myself!)
  • Narang
  • Lopez de Prado
  • Dale Rosenthal (UIUC) execution course slides
  • Watch out for academic books about modeling (eg Campbell, Cochrane)

Historical / Completed

2020

• MIT 15.501 Introduction to Financial and Managerial Accounting

2019

• MIT 14.01 Principles of Microeconomics

• MIT 18.600 Probability and Random Variables

• C++ programming practice
  • some Leetcode questions
  • some C++ books including:
    • Effective C++
    • Effective STL
    • Effective Modern C++
    • A Tour of C++

2018

I bought a bunch of books but didn’t get through most of them.

Skimmed:

• 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…

2017

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)

Abandoned

2020

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.

2019

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