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
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UL: Unsupervised Learning
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PSD: Positive Semi-Definite
- MCTC: Markov Chain Monte Carlo
- To understand bias-variance “trade-off”, 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 - Bias-Variance Tradeoff of Caltech’s Machine Learning Course - CS 156 (Spring 2012) by Professor Yaser Abu-Mostafa
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