Intro to 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
- 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
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