Search results
Results from the WOW.Com Content Network
Key topics include machine learning, deep learning, natural language processing and computer vision. Many universities now offer specialized programs in AI engineering at both the undergraduate and postgraduate levels, including hands-on labs, project-based learning, and interdisciplinary courses that bridge AI theory with engineering practices.
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 ]
Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.
Data science involves the application of machine learning to extract knowledge from data. Subfields of machine learning include deep learning, supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and active learning. Causal inference is another related component of information engineering.
It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
AI-assisted reverse engineering (AIARE) is a branch of computer science that leverages artificial intelligence (AI), notably machine learning (ML) strategies, to augment and automate the process of reverse engineering. The latter involves breaking down a product, system, or process to comprehend its structure, design, and functionality.
Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...