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Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
RULES-5 [8] is the first RULES version that handles continuous attributes without discretization. It was also extended to produce RULES-5+[9], which improves the performance using a new rule space representation scheme. RULES-6 [10] is a scalable version of RULES family developed as an extension of RULES-3 plus.
One example is the genetic algorithm for optimizing coefficients of a PID controller [2] or discrete-time optimal control. [ 3 ] Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing , feature engineering , feature extraction , and ...
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial ...
An example is Multi-view Classification based on Consensus Matrix Decomposition (MCMD), [2] which mines a common clustering scheme across multiple datasets. MCMD is designed to output two types of class labels (scale-variant and scale-invariant clustering), and: is computationally robust to missing information, can obtain shape- and scale-based ...
A basic example of this is quantum state tomography, where a quantum state is learned from measurement. [1] Other examples include learning Hamiltonians, [2] [3] learning quantum phase transitions, [4] [5] and automatically generating new quantum experiments.