Machine Learning - Getting Started

Machine learning is a branch of artificial intelligence. The research on artificial intelligence follows a natural and clear path from focusing on "reasoning" to focusing on "knowledge" and then on to "learning". Obviously, machine learning is a way to realize artificial intelligence, that is, using machine learning as a means to solve problems in artificial intelligence. In the past 30 years, machine learning has developed into a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, computational complexity theory and other disciplines. Machine learning theory mainly involves the design and analysis of algorithms that allow computers to "learn" automatically. Machine learning algorithms are a type of algorithm that automatically analyze and obtain patterns from data and use the patterns to predict unknown data. Because learning algorithms involve a large number of statistical theories, machine learning is particularly closely related to inferential statistics, also known as statistical learning theory. In terms of algorithm design, machine learning theory focuses on achievable and effective learning algorithms. Many inference problems are difficult to follow without a program, so part of machine learning research is to develop tractable approximate algorithms.

Machine learning has been widely used in data mining, computer vision, natural language processing, biometric identification, search engines, medical diagnosis, detecting credit card fraud, securities market analysis, DNA sequence sequencing, speech and handwriting recognition, strategic games and robots and other fields.

Machine learning has the following definitions:

• Machine learning is a science of artificial intelligence. The main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms in empirical learning.
• Machine learning is the study of computer algorithms that can automatically improve through experience.
• Machine learning uses data or past experience to optimize the performance standards of computer programs.

A frequently cited English definition is: 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.

Machine learning can be divided into the following categories:

• Supervised learning learns a function from a given training data set. When new data arrives, the result can be predicted based on this function. The training set requirement of supervised learning is to include input and output, which can also be said to be features and targets. The objects in the training set are labeled by humans. Common supervised learning algorithms include regression analysis and statistical classification.

The difference between supervised learning and unsupervised learning is whether the training set target is human-labeled. They all have training sets and both have input and output

• Compared with supervised learning, unsupervised learning has no human-labeled results in the training set. A common unsupervised learning algorithm is clustering.
• Semi-supervised learning is between supervised learning and unsupervised learning.
• Reinforcement learning learns how to perform actions through observation. Each action has an impact on the environment, and learning subjects make judgments based on the feedback they observe from the surrounding environment.

• Bishop, C. M. (1995). "Neural Networks for Pattern Recognition", Oxford University Press. ISBN 0-19-853864-2.
• Bishop, C. M. (2006). "Pattern Recognition and Machine Learning", Springer. ISBN 978-0-387-31073-2.
• Richard O. Duda, Peter E. Hart, David G. Stork (2001). "Pattern Classification" (2nd Edition), New York: Wiley. ISBN 0-471-05669-3.
• MacKay, D. J. C. (2003). "Information Theory, Reasoning and Learning Algorithms", Cambridge University Press. ISBN 0-521-64298-1
• Mitchel.l, T. (1997). "Machine Learning", McGraw Hill. ISBN 0-07-042807-7
• Sholom Weiss, Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5.

The above is the detailed content of Machine Learning - Getting Started. For more information, please follow other related articles on the PHP Chinese website!