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Machine learning (ML) is a field of artificial intelligence that develops and studies algorithms that can learn from data and generalize to unseen data. ML has many applications in various fields, such as natural language processing, computer vision, and medicine, and is related to data mining, statistics, and neural networks.
Online machine learning is a method of learning from sequential data and updating the best predictor at each step. Learn about the statistical and adversarial views, the algorithms, the examples and the challenges of online learning.
A foundation model is a machine learning or deep learning model that is trained on broad data and can be applied across a wide range of use cases. Learn about the origin, development, and applications of foundation models, as well as the legal definitions and challenges of this AI paradigm.
A comprehensive overview of the history of machine learning, from its origins in statistics and artificial intelligence to its modern applications and challenges. Explore the major discoveries, achievements, milestones and events in machine learning across different decades and fields.
A neural network is a model inspired by the structure and function of biological neural networks in animal brains. It consists of connected units or nodes called artificial neurons, which can learn from experience and perform various tasks such as predictive modeling and problem solving.
Learn about active learning, a special case of machine learning where a learner can interactively query a human user or other information source for labels. Explore different scenarios, query strategies and algorithms for active learning.
Federated learning is a machine learning technique that trains a model on decentralized data without exchanging data samples. It involves iterative rounds of local training, model updates, and aggregation, and can handle data heterogeneity, privacy, and access issues.
Multimodal learning is a deep learning technique that uses multiple modalities of data, such as text, audio, or images. Learn about multimodal transformers, multimodal large language models, multimodal deep Boltzmann machines, and their applications in computer vision, speech recognition, and natural language processing.