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。

灵活的计划

设置并保持灵活的截止日期。

中级

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode

完成时间大约为2 个月

建议 14 小时/周

英语(English)

字幕:英语(English)

您将学到的内容有

  • Check

    Build a Reinforcement Learning system for sequential decision making.

  • Check

    Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).

  • Check

    Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.

  • Check

    Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning 

您将获得的技能

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

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。

中级

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode

完成时间大约为2 个月

建议 14 小时/周

英语(English)

字幕:英语(English)

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 4 门课程

课程1

Fundamentals of Reinforcement Learning

4.8
54 个评分
20 个审阅
课程2

Sample-based Learning Methods

课程3

Prediction and Control with Function Approximation

课程4

A Complete Reinforcement Learning System (Capstone)

讲师

Avatar

Martha White

Assistant Professor
Computing Science
Avatar

Adam White

Assistant Professor
Computing Science

关于 阿尔伯塔大学

UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences....

关于 Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

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。

  • It is recommended that learners take between 4-6 months to complete the specialization.

  • Recommended that learners have at least one year of undergraduate computer science or 2-3 years of professional experience in software development. Experience and comfort with programming in Python required. Must be comfortable converting algorithms and pseudocode into Python. Basic understanding of concepts from statistics (distributions, sampling, expected values), linear algebra (vectors and matrices), and calculus (computing derivatives)

  • Yes, it is recommended that courses are taken sequentially.

  • Learners that complete the specialization will earn a Coursera specialization certificate signed by the professors of record, not a University of Alberta credit.

  • By the end of this specialization, you will be able to"

    • Build a Reinforcement Learning system for sequential decision making.
    • Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).
    • Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.
    • Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning 

还有其他问题吗?请访问 学生帮助中心