Methods and principles of building "computer systems that try to automatically improve with experience."
From The Discipline of Machine Learning by Tom Mitchell:
The field of Machine Learning seeks to answer the question "How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?" This question covers a broad range of learning tasks, such as how to design autonomous mobile robots that learn to navigate from their own experience, how to data mine historical medical records to learn which future patients will respond best to which treatments, and how to build search engines that automatically customize to their user's interests. To be more precise, we say that a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.
High level machine learning problems include:
- supervised learning (tag);
- unsupervised learning (tag);
- semi-supervised learning (tag);
- outlier or anomaly detection (tag); and
- reinforcement learning (tag).
The following threads have details of references on the subject:
- Can you recommend a book to read before Elements of Statistical Learning?
- Machine learning cookbook / reference card / cheatsheet?
The following journals are dedicated to research in Machine Learning: