Collectives Machine Learning article
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This article introduces the basics of machine learning theory, laying down the common concepts and techniques involved. This post is intended for the people starting with machine learning, making it easy to follow the core concepts and get comfortable with machine learning basics.
This article introduces the basics of machine learning theory, laying down the common concepts and techniques involved. This post is intended for the people starting with machine learning, making it easy to follow the core concepts and get comfortable with machine learning basics.
Source
What is Machine Learning?
In 1959, Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, described machine learning as “the study that gives computers the ability to learn without being explicitly programmed.”
Alan Turing’s seminal paper (Turing, 1950) introduced a benchmark standard for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a manner that cannot be differentiated from that of a human being.
Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. The performance of such a system should be at least human level.
A more technical definition given by Tom M. Mitchell’s (1997) : “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Example:
A handwriting recognition learning problem:Task T: recognizing and classifying handwritten words within images
Performance measure P: percent of words correctly classified, accuracy
Training experience E: a data-set of handwritten words with given classifications
In order to perform the task T, the system learns from the data-set provided. A data-set is a collection of many examples. An example is a collection of features.
Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. The performance of such a system should be at least human level.
Machine Learning Categories
Machine Learning is generally categorized into three types: Supervised Learning, Unsupervised Learning, Reinforcement learning
Machine Learning is generally categorized into three types: Supervised Learning, Unsupervised Learning, Reinforcement learning:
Supervised Learning:
Supervised Learning:
In supervised learning the machine experiences the examples along with the labels or targets for each example. The labels in the data help the algorithm to correlate the features.
In supervised learning the machine experiences the examples along with the labels or targets for each example. The labels in the data help the algorithm to correlate the features.
Two of the most common supervised machine learning tasks are classification and regression.
In classification problems the machine must learn to predict discrete values. That is, the machine must predict the most probable category, class, or label for new examples. Applications of classification include predicting whether a stock's price will rise or fall, or deciding if a news article belongs to the politics or leisure section. In regression problems the machine must predict the value of a continuous response variable. Examples of regression problems include predicting the sales for a new product, or the salary for a job based on its description.
Unsupervised Learning:
Unsupervised Learning:
When we have unclassified and unlabeled data, the system attempts to uncover patterns from the data . There is no label or target given for the examples. One common task is to group similar examples together called clustering.
When we have unclassified and unlabeled data, the system attempts to uncover patterns from the data . There is no label or target given for the examples. One common task is to group similar examples together called clustering.
Reinforcement Learning:
Reinforcement Learning:
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal. For example, maximize the points won in a game over many moves.