關於此 專項課程
51,960 次近期查看

100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

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

完成時間大約為2 個月

建議 11 小時/週

英語(English)

字幕:英語(English)

您將學到的內容有

  • 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 

您將獲得的技能

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

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

完成時間大約為2 個月

建議 11 小時/週

英語(English)

字幕:英語(English)

專項課程的運作方式

加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。

實踐項目

每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。

獲得證書

在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

how it works

此專項課程包含 4 門課程

課程1

Fundamentals of Reinforcement Learning

4.8
465 個評分
127 條評論
課程2

Sample-based Learning Methods

4.8
200 個評分
44 條評論
課程3

Prediction and Control with Function Approximation

4.8
109 個評分
15 條評論
課程4

A Complete Reinforcement Learning System (Capstone)

4.6
65 個評分
11 條評論

講師

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Martha White

Assistant Professor
Computing Science
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Adam White

Assistant Professor
Computing Science

關於 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....

關於 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....

常見問題

  • 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 

還有其他問題嗎?請訪問 學生幫助中心