Intro on Machine Learning

Machine learning is sub set of artificial intelligence and it is study of systems that can learn from data. A machine learning system could be trained. Core of machine learning deals with representation and generalization.

Machine learning is a "Field of study that gives computers the ability to learn without being explicitly programmed". A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set.

What is the different of machine learning and data mining?

• Machine learning focuses on prediction, based on known properties learned from the training data.
• Data mining focuses on the discovery of (previously) unknown properties in the data. (This is the analysis step of Knowledge Discovery in Databases)
• Data mining uses many machine learning methods, but often with a slightly different goals
• Machine learning also used data mining methods as "unsupervised learning" to improve learner accuracy

Algorithm types

• Supervised learning (labelled)
• Unsupervised learning (unlabelled)
• Semi-supervised learning
• Transduction (reasoning from observed)
• Learning to learn (multi-task learning)
• Reinforcement learning
• Developmental learning (imitation)

Applications for machine learning

• Computer vision (object recognition)
• Natural language processing
• Syntactic pattern recognition
• Search engines
• Medical diagnosis
• Detecting credit card fraud
• Stock market analysis
• Speech and handwriting recognition
• Game playing
• Software engineering
• Adaptive websites
• Computational advertising
• Computational finance

[1] http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/

[2] http://inside-bigdata.com/2014/04/18/quantum-machine-learning/