关于此 专项课程

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100% 在线课程

立即开始,按照自己的计划学习。

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高级

英语(English)

字幕:英语(English), 韩语

100% 在线课程

立即开始,按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

高级

英语(English)

字幕:英语(English), 韩语

专项课程的运作方式

加入课程

Coursera 专项课程是帮助您掌握一门技能的一系列课程。若要开始学习,请直接注册专项课程,或预览专项课程并选择您要首先开始学习的课程。当您订阅专项课程的部分课程时,您将自动订阅整个专项课程。您可以只完成一门课程,您可以随时暂停学习或结束订阅。访问您的学生面板,跟踪您的课程注册情况和进度。

实践项目

每个专项课程都包括实践项目。您需要成功完成这个(些)项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程,则需要在开始之前完成其他所有课程。

获得证书

在结束每门课程并完成实践项目之后,您会获得一个证书,您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。

how it works

此专项课程包含 7 门课程

课程1

课程 1

Introduction to Deep Learning

4.6
1,264 个评分
281 条评论
课程2

课程 2

How to Win a Data Science Competition: Learn from Top Kagglers

4.7
788 个评分
164 条评论
课程3

课程 3

Bayesian Methods for Machine Learning

4.6
474 个评分
128 条评论
课程4

课程 4

Practical Reinforcement Learning

4.2
304 个评分
81 条评论

讲师

授课教师 Mikhail Hushchyn 的图片

Mikhail Hushchyn 

Researcher at Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science
授课教师 Alexey Zobnin 的图片

Alexey Zobnin 

Accosiate professor
HSE Faculty of Computer Science
授课教师 Alexey Artemov 的图片

Alexey Artemov 

Senior Lecturer
HSE Faculty of Computer Science
授课教师 Sergey Yudin 的图片

Sergey Yudin 

Analyst-developer
Yandex
授课教师 Alexander Guschin 的图片

Alexander Guschin 

Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science
授课教师 Nikita Kazeev 的图片

Nikita Kazeev 

Researcher
HSE Faculty of Computer Science
授课教师 Andrei Ustyuzhanin 的图片

Andrei Ustyuzhanin 

Head of Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science
授课教师 Dmitry Ulyanov 的图片

Dmitry Ulyanov 

Visiting lecturer
HSE Faculty of Computer Science
授课教师 Marios Michailidis 的图片

Marios Michailidis 

Research Data Scientist
H2O.ai
授课教师 Daniil Polykovskiy 的图片

Daniil Polykovskiy 

Sr. Research Scientist
HSE Faculty of Computer Science
授课教师 Ekaterina Lobacheva 的图片

Ekaterina Lobacheva 

Senior Lecturer
HSE Faculty of Computer Science
授课教师 Andrei Zimovnov 的图片

Andrei Zimovnov 

Senior Lecturer
HSE Faculty of Computer Science
授课教师 Alexander Novikov 的图片

Alexander Novikov 

Researcher
HSE Faculty of Computer Science
授课教师 Dmitry Altukhov 的图片

Dmitry Altukhov 

Visiting lecturer
HSE Faculty of Computer Science
授课教师 Pavel Shvechikov 的图片

Pavel Shvechikov 

Researcher at HSE and Sberbank AI Lab
HSE Faculty of Computer Science
授课教师 Anton Konushin 的图片

Anton Konushin 

Senior Lecturer
HSE Faculty of Computer Science
授课教师 Anna Kozlova 的图片

Anna Kozlova 

Team Lead
Yandex
授课教师 Mikhail Trofimov 的图片

Mikhail Trofimov 

Visiting lecturer
HSE Faculty of Computer Science
授课教师 Evgeny Sokolov 的图片

Evgeny Sokolov 

Senior Lecturer
HSE Faculty of Computer Science
授课教师 Alexander Panin 的图片

Alexander Panin 

Lecturer
HSE Faculty of Computer Science
授课教师 Anna Potapenko 的图片

Anna Potapenko 

Researcher
HSE Faculty of Computer Science

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关于 国立高等经济大学

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

常见问题

  • 可以!点击您感兴趣的课程卡开始注册即可。注册并完成课程后,您可以获得可共享的证书,或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分,系统会自动为您订阅完整的专项课程。访问您的学生面板,跟踪您的进度。

  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 8-10 months.

  • As prerequisites we assume calculus and linear algebra (especially derivatives, matrices and operations with them), probability theory (random variables, distributions, moments), basic programming in python (functions, loops, numpy), basic machine learning (linear models, decision trees, boosting and random forests). Our intended audience are all people who are already familiar with basic machine learning and want to get a hand-on experience of research and development in the field of modern machine learning.

  • We recommend taking the “Intro to Deep Learning” course first as most of the subsequent courses will build on its material. All other courses can be taken in any order.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • After completing 7 courses of the Specialization you will be able to:

    Use modern deep neural networks for various machine learning problems with complex inputs;

    Participate in data science competitions and use the most popular and effective machine learning tools;

    Adopt the best practices of data exploration, preprocessing and feature engineering;

    Perform Bayesian inference, understand Bayesian Neural Networks and Variational Autoencoders;

    Use reinforcement learning methods to build agents for games and other environments;

    Solve computer vision problems with a combination of deep models and classical computer vision algorithms;

    Outline state-of-the-art techniques for natural language tasks, such as sentiment analysis, semantic slot filling, summarization, topics detection, and many others;

    Build goal-oriented dialogue agents and train them to hold a human-like conversation;

    Understand limitations of standard machine learning methods and design new algorithms for new tasks.

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