# 0-1: Preface Hi, I'm an undergraduate junior student from NTUEE when I start to write this note. I hope that my notes are not just for application but also covers the theoretical side of ML. The following are my studying reference resources: * M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning,” the MIT Press, 2012. * S. Shalev-Shwartz and S. Ben-David, “Understanding Machine Learning: From Theory to Algorithms,” Cambridge University Press, 2014. * Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning : with Applications in R.,” New York :Springer, 2013. ML Lecture Resources from NTU professors includes: * Hung-Yi Lee * I-Hsiang Wang * Hsuan-Tien Lin * Yun-Nung (Vivian) Chen 哈囉,目前我是台大電機系大三的學生。這是我自學機器學習(並在後面接續上課)所寫的ML筆記,希望可以從理論到實際應用的層面都能夠有完整的整理。 目前我正在參考以下教授的學習資料: * 機器學習(李宏毅) (他應該有名到大家都聽過吧) * 深度學習之應用(陳媪儂) * 機器學習基石/技法(林軒田) * 機器學習中的數學原理(王奕翔) 我後面大概不會寫到中文了吧(可能一些名詞會翻譯一下),還請各位讀者見諒XD ## Outline of this note Chapter 1 explores the theoretical foundation of ML, so called **statistical learning theory**. We'll explain the *PAC learning framework*, know why can computer learn stuffs, then present the learning gaurantee when the hypothesis is finite. If the hypothesis set is not finite, we'll introduce the *VC theory* and a more modern complexity mearsure, *Rademacher complexity*, which are powerful tools to show the learning guarantee of infinite hypothesis set. ###### tags: `machine learning`