Examples of using Learning theory in English and their translations into Russian
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Gogol's works in context of nonclassical learning theory.
Students start by learning theory and business fundamentals in the classroom.
This is the recursion-theoretic branch of learning theory.
Computational learning theory has led to several practical algorithms.
There are several different approaches to computational learning theory.
Learning theory and practice of innovation management and technology commercialization;
Special events for bachelor students"Statistical Learning Theory Days", March 15, 22, April 12, 14, 2018.
Learning theory in the past decade has expanded dramatically because of the introduction of multimedia.
From the perspective of statistical learning theory, supervised learning is best understood.
Our main goal is to offer students a full range of courses that encompass modern statistical learning theory.
The Statistical Learning Theory is a joint MSc program of Higher School of Economics(HSE) and Skoltech.
We expect productive cooperation with leading specialists in machine learning theory from around the world.
Statistical learning theory deals with the problem of finding a predictive function based on data.
Case Learning allows students to develop the skills andcapacity to solve real-life problems, learning theory through practice.
Machine learning theory and artificial intelligence are dynamic and rapidly developing areas of modern science.
In addition to performance bounds, computational learning theory studies the time complexity and feasibility of learning. .
Research on kernel methods, support vector machines, neural networks, machine vision, bioinformatics,computational learning theory.
In computational learning theory, a computation is considered feasible if it can be done in polynomial time.
Typically, their statistical properties are analyzed using statistical learning theory for example, using Rademacher complexity.
Research on machine learning theory, kernel methods for text analysis, support vector machines, kernel theory. .
Autism therapies Applied behavior analysis(ABA)- a science that involves using modern behavioral learning theory to modify behaviors.
Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, bioinformatics and baseball.
The computational analysis of machine learning algorithms andtheir performance is a branch of theoretical computer science known as computational learning theory.
His transformational learning theory greatly expands the possibilities of analyzing regularities and predicting individual development and systemic progress.
The discovery of the link between mirror neurons andsocial cognition provides further links to a neurological basis found in other social phenomena such as social learning theory, empathy, and observational learning. .
In 2017, the Higher School of Economics(HSE) and Skoltech launched a Statistical Learning Theory MSc program designed to prepare students for scientific research at the intersection of mathematics and computer science.
Computational learning theory- In computer science,computational learning theory(or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
The different approaches include: Exact learning, proposed by Dana Angluin; Probably approximately correct learning(PAC learning), proposed by Leslie Valiant; VC theory, proposed by Vladimir Vapnik and Alexey Chervonenkis; Bayesian inference;Algorithmic learning theory, from the work of E. Mark Gold; Online machine learning, from the work of Nick Littlestone.
He is the originator of Connectivism theory andauthor of the article Connectivism: A Learning Theory for the Digital Age and the book Knowing Knowledge- an exploration of the impact of the changed context and characteristics of knowledge.
Statistical learning theory takes the perspective that there is some unknown probability distribution over the product space Z X× Y{\displaystyle Z=X\times Y}, i.e. there exists some unknown p( z) p( x→, y){\displaystyle p( z)= p{\ vec{ x}}, y.