Machine learning and data mining

Problems:

Classification, clustering, Regression, Anomaly Detection, association rules,

Reinforcement learning, Structurd prediction, Feature Learning, Online learning,

Semi-supervised Learning, Grammar induction

Supervised Learning:

Decision Trees, ensembles (Bagging, boostring, Random Forest), k-mn, Linear regression,

Native Bayes, nenural networks, Logistic regression, Perceptron,

Support Vector Machine (SVM), Relevance vector machine (RVM)

Clustering:

BIRCH, Hierachical, K-means, Expectation-maximization (EM), DBSCAN, OPTICS, Mean-shift

dimensionality reduction:

Factor analysis, CCA, ICA, LDA, NMF, PCA, T-sne

Structured prediction:

Graphical Models (Bayes NET, CRF, HMM)

Anomaly Detection:

K-MN, Local outlier factor

Neural nets:

Autoencoder, learning, Multiayer perceptron, RNN, Restricted Boltzmann machine,

SOM, convolutional Neural network

Theory

Bias-variance dilemma, Computational learnig theory, empirical risk minimization,

PAC Learning, statistical learning, VC theory

This article is from the "Koala Programmer" blog, so be sure to keep this source http://koala87.blog.51cto.com/8339141/1637785

Machine learning and data mining