このコースについて

36,321 最近の表示
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル

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

約23時間で修了
英語
字幕:英語

習得するスキル

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル

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

約23時間で修了
英語
字幕:英語

提供:

アルバータ大学(University of Alberta) ロゴ

アルバータ大学(University of Alberta)

Alberta Machine Intelligence Institute ロゴ

Alberta Machine Intelligence Institute

シラバス - 本コースの学習内容

1

1

1時間で修了

Welcome to the Final Capstone Course!

1時間で修了
2件のビデオ (合計10分), 2 readings
2件のビデオ
Meet your instructors!8 分
2件の学習用教材
Reinforcement Learning Textbook10 分
Pre-requisites and Learning Objectives10 分
2

2

1時間で修了

Milestone 1: Formalize Word Problem as MDP

1時間で修了
4件のビデオ (合計23分)
4件のビデオ
Andy Barto on What are Eligibility Traces and Why are they so named?9 分
Let's Review: Markov Decision Processes6 分
Let's Review: Examples of Episodic and Continuing Tasks3 分
3

3

1時間で修了

Milestone 2: Choosing The Right Algorithm

1時間で修了
7件のビデオ (合計40分)
7件のビデオ
Let's Review: Expected Sarsa3 分
Let's Review: What is Q-learning?3 分
Let's Review: Average Reward- A New Way of Formulating Control Problems10 分
Let's Review: Actor-Critic Algorithm5 分
Csaba Szepesvari on Problem Landscape8 分
Andy and Rich: Advice for Students5 分
1の練習問題
Choosing the Right Algorithm
4

4

1時間で修了

Milestone 3: Identify Key Performance Parameters

1時間で修了
4件のビデオ (合計25分)
4件のビデオ
Let's Review: Non-linear Approximation with Neural Networks4 分
Drew Bagnell on System ID + Optimal Control6 分
Susan Murphy on RL in Mobile Health7 分
1の練習問題
Impact of Parameter Choices in RL40 分

レビュー

A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE) からの人気レビュー

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強化学習専門講座について

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more....
強化学習

よくある質問

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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