# 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

edit- Bird's Eye View
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Statistics Fundamentals
- Artificial Neural Networks
- Inductive Inference
- PAC Learning
- VC Dimension
- Artificial Neural Network

## Offsite courses

edit### MIT Open Learning Library

edit- Introduction to Machine Learning , undergraduate course 6.036.

### Mathematical Monk

edit- Machine Learning, 160 video lectures hosted on Youtube.

### Lecture notes

edit*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

edit### Wikipedia

edit- 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

### Textbooks

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