Über diesen Kurs

35,345 kürzliche Aufrufe
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“

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

Ca. 23 Stunden zum Abschließen
Englisch
Untertitel: Englisch

Kompetenzen, die Sie erwerben

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“

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

Ca. 23 Stunden zum Abschließen
Englisch
Untertitel: Englisch

von

University of Alberta-Logo

University of Alberta

Alberta Machine Intelligence Institute-Logo

Alberta Machine Intelligence Institute

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1

Woche 1

1 Stunde zum Abschließen

Welcome to the Final Capstone Course!

1 Stunde zum Abschließen
2 Videos (Gesamt 10 min), 2 Lektüren
2 Videos
Meet your instructors!8m
2 Lektüren
Reinforcement Learning Textbook10m
Pre-requisites and Learning Objectives10m
Woche
2

Woche 2

1 Stunde zum Abschließen

Milestone 1: Formalize Word Problem as MDP

1 Stunde zum Abschließen
4 Videos (Gesamt 23 min)
4 Videos
Andy Barto on What are Eligibility Traces and Why are they so named?9m
Let's Review: Markov Decision Processes6m
Let's Review: Examples of Episodic and Continuing Tasks3m
Woche
3

Woche 3

1 Stunde zum Abschließen

Milestone 2: Choosing The Right Algorithm

1 Stunde zum Abschließen
7 Videos (Gesamt 40 min)
7 Videos
Let's Review: Expected Sarsa3m
Let's Review: What is Q-learning?3m
Let's Review: Average Reward- A New Way of Formulating Control Problems10m
Let's Review: Actor-Critic Algorithm5m
Csaba Szepesvari on Problem Landscape8m
Andy and Rich: Advice for Students5m
1 praktische Übung
Choosing the Right Algorithm
Woche
4

Woche 4

1 Stunde zum Abschließen

Milestone 3: Identify Key Performance Parameters

1 Stunde zum Abschließen
4 Videos (Gesamt 25 min)
4 Videos
Let's Review: Non-linear Approximation with Neural Networks4m
Drew Bagnell on System ID + Optimal Control6m
Susan Murphy on RL in Mobile Health7m
1 praktische Übung
Impact of Parameter Choices in RL40m

Bewertungen

Top-Bewertungen von A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)

Alle Bewertungen anzeigen

Über den Spezialisierung Verstärkungslernen

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....
Verstärkungslernen

Häufig gestellte Fragen

  • 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.

Haben Sie weitere Fragen? Besuchen Sie das Hilfe-Center für Teiln..