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2. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   A machine learning model is a type of mathematical model which, ... Different machine learning approaches can suffer from different data biases. A machine learning ...

3. Deep learning - Wikipedia

   en.wikipedia.org/wiki/Deep_learning

   Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]

4. Outline of machine learning - Wikipedia

   en.wikipedia.org/wiki/Outline_of_machine_learning

   The following outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [1] In 1959, Arthur Samuel defined machine learning as a "field of ...

5. Random forest - Wikipedia

   en.wikipedia.org/wiki/Random_forest

   Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction ...

6. Meta-learning (computer science) - Wikipedia

   en.wikipedia.org/wiki/Meta-learning_(computer...

   Meta learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning ...

7. Supervised learning - Wikipedia

   en.wikipedia.org/wiki/Supervised_learning

   Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]

8. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]

9. Rule-based machine learning - Wikipedia

   en.wikipedia.org/wiki/Rule-based_machine_learning

   v. t. e. Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1] [2] [3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...