Notes on Machine Learning (master page)
by 장승환
This is the master page for Notes on Machine Learning posts, in which I summarize in a succinct and straighforward fashion what I learn from Machine Learning course by Mathematical Monk, along with my own thoughts and related resources.
 Notes on Machine Learning 1: What is machine learning?
 Notes on Machine Learning 2: Decision trees
 Notes on Machine Learning 3: Decision theory
 Notes on Machine Learning 4: Maximum Likelihood Estimation
 Notes on Machine Learning 5: MLE for Exponential Families
 Notes on Machine Learning 6: Maximum a posteriori (MAP) estimation
 Notes on Machine Learning 7: Bayesian Inference
 Notes on Machine Learning 8: Naive Bayes
To be added..
Acronyms
 MM: Mathematical Monk
 ML: Machine Learning
 SL: Supervised Learning

UL: Unsupervised Learning

PSD: Positive SemiDefinite
 MCTC: Markov Chain Monte Carlo
 To understand biasvariance “tradeoff”, take a quick route:
(11.5) $\leadsto$ (11.1) (11.2) (11.3) (11.4) $\leadsto$ (11.1) (11.2)
Some other helpful resources:
 Lecture 08  BiasVariance Tradeoff of Caltech’s Machine Learning Course  CS 156 (Spring 2012) by Professor Yaser AbuMostafa
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