Study Tracking
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.
Planned
CS
- C compiler
- Dragon book
- Engineering a Compiler book
- Computer Networks
- Monte Carlo simulations
- Physics simulations
Math
- “Honors” mathematical statistics
- MIT 18.650
- CMU 36-705
- Stanford Stats 200
- Applied statistics
- CMU 36-707
- Stanford Stats 203
- Stochastic processes / “honors” discrete math
- Statistical / “honors” ML
- Information theory
- Differential equations
- Fourier analysis
Finance
- Investing
- Some kind of practical quant finance / advanced investing
- Programming projects (do it myself!)
- Lopez de Prado
- Trading systems books
Completed
2021
- MIT 6.046 Analysis of Algorithms
Major study books:
- CLRS, Introduction to Algorithms
- All starred and end of chapter problems completed
Supplemental books:
2020
Major study books:
- Abelson & Sussman, SICP
- All written exercises complete
- Coding exercises through partway Ch 3 complete
- CLRS, Introduction to Algorithms
- All starred and end of chapter problems completed through Ch 17
Supplemental books:
- Skiena, The Algorithm Design Manual
- Knuth, The Art of Computer Programming: Volume 1
- Pratt, Financial Accounting in an Economic Context
2019
Supplemental books:
- Varian, Intermediate Microeconomics
- Ross, A First Course in Probability
- Murphy, Machine Learning: a Probabilistic Perspective
- Goodfellow et al, Deep Learning
- Rice, Mathematical Statistics and Data Analysis
2018
I bought a bunch of books but didn’t get through most of them. Nothing
organized completed this year.
Supplemental books:
- Russell & Norvig, Artificial Intelligence: A Modern Approach
- Murphy, Machine Learning: a Probabilistic Perspective
- Hastie et al, The Elements of Statistical Learning
- Hastie et al, Introduction to Statistical Learning
- Rice, Mathematical Statistics and Data Analysis
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
while preparing to find a job.
- Probability (MIT 6.041 Applied Probability)
- Statistics (DeGroot & Schervish, Ch 6-8)
- ML (Hastie et al, Ch 1-3)
- 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
Goals (2019)
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
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).