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 | edit source]
- Bird's Eye View
- Reinforcement Learning
- Unsupervised Learning
- Supervised Learning
- Statistics Fundamentals
- Artificial Neural Networks
- Inductive Inference
- PAC Learning
- VC Dimension
Offsite courses[edit | edit source]
Mathematical Monk[edit | edit source]
- Machine Learning, 160 video lectures hosted on Youtube.
Udacity[edit | edit source]
- Machine Learning—Supervised, Unsupervised, and Reinforcement Learning
- Machine Learning—Unsupervised Learning
- Machine Learning—Reinforcement Learning
Lecture notes[edit | edit source]
- Foundations of Machine Learning and Data Science, Nina Balcan and Avrim Blum, Carnegie Mellon University, Fall 2015
Readings[edit | edit source]
Wikipedia[edit | edit source]
- 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
See also[edit | edit source]
External Links[edit | edit source]
- MachineLearning on Reddit
- Sample 'R' code demonstrating key concepts in machine learning on text data