Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science. Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances...
Devenez un scientifique des données UX! De l'analyse de données qualitatives à l'analyse du « Big Data », vous serez en mesure de dégager des « insights » des données afin de formuler des recommandations sur des bases empiriques. Le « Big Data » et l'UX vous interpellent? Ce MOOC vous donnera les méthodes...
Become a UX data scientist! From qualitative data analysis to big data Web analytics, you will be able to leverage insights from data to make empirically-based recommendations. Do big data and UX speak to you? This MOOC will give you the methods and tools to analyze the whole spectrum of data we handle...
Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R. Modern finance is the science of decision making in an uncertain world, and its language...
Financial derivatives are ubiquitous in global capital markets. Students will obtain a sophisticated understanding of valuation methods; tools for quantifying, hedging, and speculating on risk; and a basic familiarity with major markets and instruments. Financial derivatives are ubiquitous in global...
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...
Обязательный курс первого семестра для отделения Computer Science.
Преподаватель - д.ф.-м.н. А.М.Райгородский.
Основы перечислительной комбинаторики
Обобщенная функция Мёбиуса и асимптотики
Деревья и унициклические графы
Разбиение чисел на слагаемые
Производящие функции и линейные ...
Лекции:
- Построение суффиксного дерева (по Укконену)
- Преобразование Берроуза-Вилера
- Архитектура поисковых систем. Pagerank
- Структура сложных сетей
- Введение в байесовские сети
- Автоматическая классификация текстов
- Метод опорных векторов (Support vector machines)
- Семантический Ве...
Курс знакомит со сложностью вероятностных вычислений и теоретическими основами криптографии. Мы изучим вероятностные классы сложности и основные приемы, которые используются для анализа и построения вероятностных алгоритмов, узнаем, что такое интерактивные протоколы, игры Артура и Мерлина, докажем з...
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 free 5-module online introductory course gives you the essential concepts, techniques and skills to effectively work with data and produce compelling data stories under tight deadlines.
Comprising of video lectures, tutorials, assignments, readings and discussion forums, this course is open to...
The exercises we cover today will have you working directly with the Spark specific components of the AMPLab’s open-source software stack, called the Berkeley Data Analytics Stack (BDAS).
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
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...
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.
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...
The course is an interdisciplinary course, focused on the emerging science of complex networks and their applications. The material includes the mathematics of networks, their applications to biology, sociology, technology and other fields, and their use in the research of real complex systems in nature...
What is credit risk? Why is it so important, in modern economies, to correctly deal with it? This course combines theory with practice to answer these questions. Imagine that you are a bank and a main part of your daily business is to lend money. Unfortunately, lending money is a risky business - there...
An introduction to probability, with the aim of developing probabilistic intuition as well as techniques needed to analyze simple random samples. Statistics 2 at Berkeley is an introductory class taken by about 1000 students each year. Stat2.2x is the second of three five-week courses that make up Stat2x...
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics. Created specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course...
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