Machine learning
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
- Bird's Eye View
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Statistics Fundamentals
- Artificial Neural Networks
- Transformer Architecture
- Inductive Inference
- PAC Learning
- VC Dimension
Offsite courses
MIT Open Learning Library
- Introduction to Machine Learning , undergraduate course 6.036.
Mathematical Monk
- Machine Learning, 160 video lectures hosted on Youtube.
Lecture notes
- Foundations of Machine Learning and Data Science, Nina Balcan and Avrim Blum, Carnegie Mellon University, Fall 2015
- Machine Learning, Rohil Singh, Tommi Jaakkola, and Ali Mohammad, MIT, Fall 2006.
Readings
Wikipedia
Cross-domain AI topics
- Attention (machine learning)
- Embedding (machine learning)
- Fairness (machine learning)
- Loss function
- Overfitting and Underfitting
- Transfer learning
- Unsupervised learning
Categories and lists:
Machine learning topics
- Machine learning
- Artificial intelligence
- Computational learning theory
- Pattern recognition
- Genetic algorithm
- Deep learning
- Artificial neural network
- List of machine learning concepts
- Journal of Machine Learning Research
- List of datasets for machine learning research
- Quantum machine learning
- Vanishing gradient problem
Textbooks
- 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
- School:Computer Science