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Machine learning

Machine learning
Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),[4] search engines and computer vision.

Unsupervised learning In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning. Unsupervised learning is closely related to the problem of density estimation in statistics.[1] However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data.

Artificial Intelligence and Machine Learning A Gaussian Mixture Model Layer Jointly Optimized with Discriminative Features within A Deep Neural Network Architecture Ehsan Variani, Erik McDermott, Georg Heigold ICASSP, IEEE (2015) Adaptation algorithm and theory based on generalized discrepancy Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina Proceedings of the 21st ACM Conference on Knowledge Discovery and Data Mining (KDD 2015) Adding Third-Party Authentication to Open edX: A Case Study John Cox, Pavel Simakov Proceedings of the Second (2015) ACM Conference on Learning @ Scale, ACM, New York, NY, USA, pp. 277-280 An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections Yu Cheng, Felix X.

Conservative Myths and the Death of Marlboro Man Those of a certain age remember TV ads featuring the Marlboro Man – a rugged individual who rode a horse through an America that even then had long since disappeared. He was self-reliant. No moocher. Didn’t need government handouts. Didn’t need government. What he needed was cigarettes.

Science Systematic enterprise that builds and organizes knowledge The Universe represented as multiple disk-shaped slices across time, which passes from left to right Modern science is typically divided into three major branches that consist of the natural sciences (e.g., biology, chemistry, and physics), which study nature in the broadest sense; the social sciences (e.g., economics, psychology, and sociology), which study individuals and societies; and the formal sciences (e.g., logic, mathematics, and theoretical computer science), which study abstract concepts. There is disagreement,[19][20][21] however, on whether the formal sciences actually constitute a science as they do not rely on empirical evidence.[22][20] Disciplines that use existing scientific knowledge for practical purposes, such as engineering and medicine, are described as applied sciences.[23][24][25][26]

Theory of Finite Automata Undergraduate course in finite automata theory with introduction to formal languages. Lecturers J.A. Garcia and S. Supervised learning Supervised learning is the machine learning task of inferring a function from labeled training data.[1] The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).

Gaussian Processes for Machine Learning: Contents Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. This book is © Copyright 2006 by Massachusetts Institute of Technology. Music Tech Fest 2013: the Festival of Music Ideas Invalid quantity. Please enter a quantity of 1 or more. The quantity you chose exceeds the quantity available. Computer science Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations History[edit] The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division.

CS 43001 Compiler Construction Course (Autumn Semester 2005) Theory Niloy Ganguly Laboratory Chitta Ranjan Mandal chitta@cse.iitkgp.ernet.inNiloy Ganguly k-means clustering k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum.

Gaussian Processes for Machine Learning: Book webpage Carl Edward Rasmussen and Christopher K. I. Williams The MIT Press, 2006.

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