This is Google’s dictionary on machine learning. Very interesting, with more than 100 entries, but clearly biased towards what Google thinks machine learning is. There is still big room for improvement, as this glossary is missing many important entries such as
- maximum likelihood
- Bayesian networks
- dimension reduction
- hierarchical modeling
- survival analysis
- Markov property
- cross-validation
- time series
Below are some of the interesting concepts listed in the Google ML glossary. You can check the full glossary (with definitions) here.
Selected entries
- backpropagation
- binary classification
- binning
- bucketing
- class-imbalanced data set
- confusion matrix
- decision boundary
- deep model
- dynamic model
- ensemble
- feature
- feature engineering
- generalized linear model
- gradient descent
- hidden layer
- hyperparameter
- input layer
- Kernel Support Vector Machines
- L1 loss
- learning rate
- least squares regression
- linear regression
- logistic regression
- machine learning
- Mean Squared Error
- mini-batch stochastic gradient descent
- model training
- neural network
- normalization
- numpy
- outliers
- output layer
- overfitting
- pandas
- precision
- prediction bias
- pre-trained model
- prior belief
- rank
- regularization
- scaling
- scikit-learn
- sigmoid function
- sparse feature
- squared loss
- stationarity
- stochastic gradient descent
- structural risk minimization
- supervised machine learning
- synthetic feature (click here for a different perspective)
- Tensor
- TensorFlow
- training set
- true negative
- unlabeled example
- unsupervised machine learning
- validation set
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