Machine learning: from the basics to advanced topics.

Includes statistics topics, data mining and artificial intelligence as well as applications like natural language processing, recommender systems, robot control.

Stanford University

Computer Science
Mathematics, Statistics and Data Analysis
Course added: long ago

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Machine Learning is now available in Coursera’s on demand format! To watch videos and complete assignments at your own pace, join the on demand course now at: https:...

University of Washington

Computer Science
Mathematics, Statistics and Data Analysis
Course added: long ago

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy! Machine learning algorithms can figure out how to perform important tasks by generalizing from...

University of Toronto

Computer Science
Mathematics, Statistics and Data Analysis
Course added: long ago

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Neural...

In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. What are Probabilistic Graphical Models?
Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will...

This course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.

University of Minnesota

Business & Management
Computer Science
Engineering & Technology
Course added: long ago

This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply...

In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems.

Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to...

This course is about building 'web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm. In the past, this course has been offered at the Indian Institute of Technology...

University of Washington

Computer Science
Engineering & Technology
Mathematics, Statistics and Data Analysis
Course added: long ago

Join the data revolution. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula. Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. #uwdatasci...

Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing." About this Course This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big...

University of Cambridge

Computer Science
Mathematics, Statistics and Data Analysis
Course added: 26 September 2013

A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms (Cambridge University Press, 2003)" which can be bought at Amazon, and is available free online. A subset of these lectures used to constitute a Part III Physics course at the University...

University of Toronto

Computer Science
Mathematics, Statistics and Data Analysis
Course added: 26 September 2013

Introductory course in machine learning by world leading expert Geoffrey Hinton. Topics include: linear regression and classification, neural networks, clustering, decision trees, gaussian processes, deep belief nets and more

University of Toronto

Computer Science
Mathematics, Statistics and Data Analysis
Course added: 26 September 2013

In this course, we study neural networks of various types. Topics include: neural network architectures, perceptrons, the backpropagation algorithm, neuro-probabilistic language models, convolutional nets for digit recognition, mini-batch gradient descent, the momentum method, recursive neural networks...

University of Toronto

Computer Science
Mathematics, Statistics and Data Analysis
Course added: 26 September 2013

Advanced course in machine learning by world leading expert Geoffrey Hinton. Topics include: graphical models, Restricted Boltzmann machines, Object Recognition in Deep Neural Nets, Recurrent neural networks, Non-linear Dimensionality Reduction and more.

Лектор: Константин Вячеславович Воронцов, старший научный сотрудник Вычислительного центра РАН. Заместитель директора по науке ЗАО "Форексис". Заместитель заведующего кафедрой «Интеллектуальные системы» ФУПМ МФТИ. Доцент кафедры "Математические методы прогнозирования" ВМиК МГУ. Эксперт компании "Янд...

Лекция 0 «Обзор основных фактов теории вероятностей»
Лекция 1
Выборка, эмпирическая вероятностная мера, теорема Гливенко-Кантелли. Описательная статистика.
Лекция 2
Статистики 1-го типа, точечные оценки, свойства точечных оценок, методы построения точечных оценок, неравенство Рао-Крамера.
...

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data...

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov...

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