Notes are available here. This is the file on which I have devoted most attention (and passion). Besides pure intellectual enjoyment, I did it hoping that it will help those who struggle with their fi

(Undergraduate) mathematical statistics, taught by Prof. Steven Heilman. Syllabus and course website Heilman’s pre-written lecture notes My lecture notes representing what the lectures covered Homewo

Real analysis / measure theory, taught by Prof. Ken Alexander. Course syllabus Lecture notes Homeworks. Disclaimer: a nontrivial portion of my solutions are flawed. Browse at your own risk. :))

Some convex analysis work done with Prof. Guillermo Reyes Souto. Sample: hw3

Actually took functional analysis this time (i.e., paid the tuition); class taught by Prof. Juhi Jang. Since only 4 people enrolled, the pace and topics covered by the class was very flexible. Here ar

Complex analysis, taught by Prof. Igor Kukavica. Course syllabus Former course website (ded link, RIP). Lecture notes — tbh I lost count of section numbers towards the end, so maybe I \chapter{&

Mathematical statistics, taught by Prof. Steven Heilman. Syllabus and course website Heilman’s pre-written lecture notes My lecture notes representing what the lectures covered Homeworks. Disclaimer:

Algorithms, taught by Prof. David Kempe. Course website and syllabus Comprehensive notes and MT/final cheatsheets can be accessed here (written for Sp2024 iteration). Not typo-proof and I’ll fix

Statistical learning theory, supervised by Prof. Steven Heilman. Registered as MATH 590 instead of 547. Syllabus and course website Again, I take no responsibility for providing false information in

Machine learning, taught by Prof. Vatsal Sharan. Course website and syllabus Lecture notes Homeworks. For questions, please visit course website. The following links only contain my (group, with Flo