Machine Learning is a term that can mean different things to different people. Andrew Ng, cofounder of Coursera and Professor at Stanford, provides two definitions in his popular Machine Learning Course. The first definition comes from Arthur Samuel around 1959.
Field of study that gives computers the ability to learn without being explicitly programmed.
The second definition comes from Tom Mitchell’s 1997 Machine Learning textbook. This definition is a bit more formal and rigorous. This book defines a well-posed learning problem as:
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Machine Learning Categories
Machine learning can be broken down into a few categories. The two most popular are supervised and unsupervised learning. A couple other categories are recommender systems and reinforcement learning.
Probably the most common category of machine learning, supervised learning is concerned with fitting a model to labeled data. Labeled data is data that has the correct answer supplied. Regression and Classification are the most common types of problems in supervised learning.
Unsupervised learning deals with unlabeled data. Therefore, the goal of unsupervised learning is to find structure in the data. Clustering is probably the most common technique.
Recommender systems deal with making recommendations based upon previously collected data. Reinforcement learning is concerned with maximizing the reward of a given agent(person, business, etc).
Most of the above information comes from the Coursera Machine Learning Course. There is still time to sign up since the first assignments are not due until the end of the week.
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