Computer Map
Contents
HRDWR
SOFTWARE
NETWORK
SECURITY
DATABASE
CONCURRENCY
INTELLIGENCE
INTERACTION
Machine learning
and
data mining
Problems
Classification
Clustering
Regression
Anomaly detection
AutoML
Association rules
Reinforcement learning
Structured prediction
Feature engineering
Feature learning
Online learning
Semi-supervised learning
Unsupervised learning
Learning to rank
Grammar induction
Supervised learning
(
classification
•
regression
)
Decision trees
Ensembles
(
Bagging
,
Boosting
,
Random forest
)
k
-NN
Linear regression
Naive Bayes
Neural networks
Logistic regression
Perceptron
Relevance vector machine (RVM)
Support vector machine (SVM)
Clustering
BIRCH
CURE
Hierarchical
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
-NN
Local outlier factor
Neural nets
Autoencoder
Deep learning
Multilayer perceptron
RNN
Restricted Boltzmann machine
SOM
Convolutional neural network
Reinforcement learning
Q-learning
SARSA
Temporal difference (TD)
Theory
Bias-variance dilemma
Computational learning theory
Empirical risk minimization
Occam learning
PAC learning
Statistical learning
VC theory
Machine-learning venues
NIPS
ICML
ML
JMLR
ArXiv:cs.LG
Related articles
List of datasets for machine-learning research
Outline of machine learning
Machine learning portal
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