people seemed to like my midterm notes so I’ll be posting few of the neat ones 🧵
easy adaptation of Bayes to machine learning with maximum a posteriori & maximum likelihood 👇🏽
Bayes optimal classifier 👇🏽
Write your own Naive Bayes classifier from scratch ✍🏻
I’ve summarized this blog post: mattmazur.com/2015/03/17/a-s…
Introduction to Reinforcement Learning 🤓 (all you need to know)
Introduction to Reinforcement Learning part 2
I'll post autoencoders & bayesian belief nets in the daylight and then comes clustering & SVMs
bayesian belief networks, because conditional independence is overrated
autoencoders 👇🏽
distance functions for different types of variables in clustering
Distance between two clusters
statistical inference (point estimation & confidence intervals) cheatsheet 💜
Hopfield networks cheatsheet 👇🏽
Feature selection cheatsheet 👇🏽
will start making my data structures & algorithms notes digital 🔜 🤩
proud nerd 🙃
Graph theory basics 👇🏽
trying something new, slowly evolving to that nerd girl in the class you ask notes from and get a higher grade than her
Runtime Analysis cheatsheet 👇🏽
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