InferenceBayesian NetworkBelief PropagationGraphical ModelMarkov Random FieldGibbs SamplingMarkov Chain Monte Carlo (MCMC)AlgorithmsExpectation–Maximization (EM) Algorithm
Специализация Графические вероятностные модели
Графические вероятностные модели. Master a new way of reasoning and learning in complex domains
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Специализация Графические вероятностные модели Стэнфордский университетПриобретаемые навыки
Специализация: общие сведения
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Проект прикладного обучения
Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks.
Карьерные результаты учащихся
43%
Начали новую карьеру, пройдя этот продукт (Специализация).
18%
Стали больше зарабатывать или получили повышение.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
Только онлайн-курсы
Начните сейчас и учитесь по собственному графику.
Гибкий график
Установите гибкие сроки сдачи заданий.
Продвинутый уровень
Предназначается для практикующих специалистов.
Approximately 4 months to complete
Suggested pace of 11 hours/week
Английский
Субтитры: Английский, Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Немецкий, Русский, Испанский
Карьерные результаты учащихся
43%
Начали новую карьеру, пройдя этот продукт (Специализация).
18%
Стали больше зарабатывать или получили повышение.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
Только онлайн-курсы
Начните сейчас и учитесь по собственному графику.
Гибкий график
Установите гибкие сроки сдачи заданий.
Продвинутый уровень
Предназначается для практикующих специалистов.
Approximately 4 months to complete
Suggested pace of 11 hours/week
Английский
Субтитры: Английский, Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Немецкий, Русский, Испанский
Специализация включает несколько курсов: 3
Probabilistic Graphical Models 1: Representation
Оценки: 1,320
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Probabilistic Graphical Models 2: Inference
Оценки: 461
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Probabilistic Graphical Models 3: Learning
Оценки: 283
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
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Стэнфордский университет
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Часто задаваемые вопросы
Получу ли я зачеты в университете за прохождение специализации?
Can I just enroll in a single course?
Можно ли зарегистрироваться только на один курс?
Can I take the course for free?
Могу ли я пройти курс бесплатно?
Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?
Сколько времени занимает получение специализации?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
Получу ли я зачеты в университете за прохождение специализации?
Остались вопросы? Посетите Центр поддержки учащихся.
