Scaling It Back
Thinking about math a lot and buying tons of math books. I think it has ballooned out of proportion and I need to scale it back. Doesn't feel quite hypo-manic, but I need to keep an eye on it.
So the idea initially was to read a few textbooks and watch a few video lectures on Linear Algebra, Probability, and Statistics so I can understand the math review chapters of data science and machine learning books. So watch MIT OCW 18.06SC, 6.041SC, and 18.650. Read Strang's Introduction to Linear Algebra, Bertsekas & Tsitsiklis's Introduction to Probability, and maybe some statistics book. Seems reasonable.
Then I thought I should review Algebra & Trigonometry and Calculus 1-3. So watch MIT OCW 18.01-03. Skim Sullivan's Algebra and Trigonometry and Strang's Calculus. Still reasonable.
I then started watching MathSocerer's videos on self-study mathematics and started including discrete math, proofs, proof-based Calculus, proof-based Linear Algebra, and Baby Rudin. Started looking at upper year Pure mathematics. I tried some proof-based advanced courses (Math 145, Math 146) at Waterloo 29 years ago and barely passed them. Not sure why I think I'd like proof-based courses now. Without winning the lottery, quitting my job, and studying full time, I'm not sure how I'd fit all this in. It no longer feels reasonable.
Okay. Sullivan's Algebra & Trigonometry, Strang's Calculus, Strang's Introduction to Linear Algebra, maybe Axler's Linear Algebra Done Right, and Bertsekas & Tsitsiklis's Introduction to Probability. Plenty of stuff there before tackling Data Science, Statistical Learning, and Machine Learning books.
So the idea initially was to read a few textbooks and watch a few video lectures on Linear Algebra, Probability, and Statistics so I can understand the math review chapters of data science and machine learning books. So watch MIT OCW 18.06SC, 6.041SC, and 18.650. Read Strang's Introduction to Linear Algebra, Bertsekas & Tsitsiklis's Introduction to Probability, and maybe some statistics book. Seems reasonable.
Then I thought I should review Algebra & Trigonometry and Calculus 1-3. So watch MIT OCW 18.01-03. Skim Sullivan's Algebra and Trigonometry and Strang's Calculus. Still reasonable.
I then started watching MathSocerer's videos on self-study mathematics and started including discrete math, proofs, proof-based Calculus, proof-based Linear Algebra, and Baby Rudin. Started looking at upper year Pure mathematics. I tried some proof-based advanced courses (Math 145, Math 146) at Waterloo 29 years ago and barely passed them. Not sure why I think I'd like proof-based courses now. Without winning the lottery, quitting my job, and studying full time, I'm not sure how I'd fit all this in. It no longer feels reasonable.
Okay. Sullivan's Algebra & Trigonometry, Strang's Calculus, Strang's Introduction to Linear Algebra, maybe Axler's Linear Algebra Done Right, and Bertsekas & Tsitsiklis's Introduction to Probability. Plenty of stuff there before tackling Data Science, Statistical Learning, and Machine Learning books.