algorithms
- decision tree
- gradient boost decision tree
- Greedy function approximation: A gradient boosting machine
- Intro. BoostedTree.pdf
- AdaBoost -ML-notes-pp:61-66
- gradient boosting
- ML-notes-pp:66-68
- Greedy function approximation: A gradient boosting machine
- wiki: Gradient_boosting
- Boosting algorithms as gradient descent
- Intro. gradient_boosting
- XGBoost
- a scalable tree boosting system
- Naive Bayesian clssification
- Gaussian naive Bayes or Gaussian discriminate analysis
- multinomial naive Bayes
- Bernoulli Bayes
- likelihood estimation of multinomial distribution
- bagging, random forests, boosting
- svm
- guide to svms
- wiki: svm
- loss function: mse, entropy, hinge
- wiki: loss function for classification
- kernel trick
- common kernel functions
- support vector regression
- svr tutor
- linear regression
- http://statweb.stanford.edu/~owen/courses/305-1415/ch2.pdf
- kernelized linear regression
- logistic regression
- least square
- lasso
- principal component analysis (PCA)
- latent dirichlet allocation (LDA) -original paper
- wiki
- independent component analysis (ICA)
- turorial
- wiki
- cs229 lecture note
- factor analysis
- cs229 lecture note
- independent factor analysis
- independent factor analysis
- k-means -wiki
- Gaussian Process
- cs229 lecture note
- the kernel cookbook
- generative models
- definitely should be the PRML
Concepts
- bias-variance trade-off
- ML notes, pp 8--11
- cs229 lecture note
- No free lunch
- List of probability
- Conjugate priors
- calculus of variations
- blog: the clculus of variations"
- ROC, PR
- wiki: ROC, PR
- matrix calculus
- matrix calculus: appendix D
- the matrix cookbook
- EM variational EM
- The Variational Approximation for Bayesian Inference
- pattern recognition and machine learning, chapter 9, 10
- lecture note
reinforcement learning
deep learning
- Goodfellow I, Bengio Y, Courville A. "Deep learning".
- github: pdf
- tutorial
- http://ufldl.stanford.edu/tutorial/
optimization
- convex optimization
- stephen boyd, "convex optimization"
- ADMM
- stephen boyd, admm
- slides
- EPFL Course - Optimization for Machine Learning - CS-439
- github
- overview of gradient descent optimization algorithms
- blog: optimizing-gradient-descent