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His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years. [ 24 ] [ 25 ] As of 2020, three of most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone (#5), Neural Networks and Deep Learning (#6).
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
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 ]
Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s: Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions – or "learn" – from the results. [2]
Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...
For example, machine learning has been used for classifying Android malware, [198] for identifying domains belonging to threat actors and for detecting URLs posing a security risk. [199] Research is underway on ANN systems designed for penetration testing, for detecting botnets, [200] credit cards frauds [201] and network intrusions.
Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was input directly (compare transfer learning). [1] In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed ...
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...