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Machine learning

From Wikiversity

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too complex to describe generally in programming languages, so that in effect programs must automatically describe programs. Artificial intelligence is a closely related field, as also probability theory and statistics, data mining, pattern recognition, adaptive control, and theoretical computer science.

Topics

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Offsite courses

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MIT Open Learning Library

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Mathematical Monk

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Lecture notes

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Readings

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Wikipedia

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Cross-domain AI topics

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Adversarial machine learning
AI alignment
Attention (machine learning)
Backpropagation
Deep learning
Embedding (machine learning)
Fairness (machine learning)
Feature learning
Fine-tuning (deep learning), SFT – supervised fine-tuning
Loss function
Mechanistic interpretability
Multilayer perceptron (MLP)
Overfitting and Underfitting
Reinforcement learning
Reinforcement learning from human feedback
Supervised learning / Unsupervised learning
Training, validation, and test data sets
Transfer learning

Categories and lists:

Artificial intelligence laboratories
Artificial intelligence companies
Glossary of artificial intelligence

Machine learning topics

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Textbooks

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  • Machine Learning by Tom Mitchell, published McGraw Hill, 1997.
  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published MIT Press, 2016.

See also

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Index

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