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

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Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
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다음 특화 과정의 4개 강좌 중 2번째 강좌:
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일정에 따라 마감일을 재설정합니다.
중급 단계

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

완료하는 데 약 28시간 필요
영어
자막: 영어

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강의 계획 - 이 강좌에서 배울 내용

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1

1

완료하는 데 1시간 필요

Welcome to the Course!

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2개 동영상 (총 10분), 2 개의 읽기 자료
2개의 동영상
Meet your instructors!8m
2개의 읽기 자료
Reinforcement Learning Textbook10m
Read Me: Pre-requisites and Learning Objectives10m
2

2

완료하는 데 4시간 필요

Monte Carlo Methods for Prediction & Control

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11개 동영상 (총 58분), 2 개의 읽기 자료, 1 개의 테스트
11개의 동영상
Using Monte Carlo for Prediction6m
Using Monte Carlo for Action Values2m
Using Monte Carlo methods for generalized policy iteration2m
Solving the Blackjack Example3m
Epsilon-soft policies5m
Why does off-policy learning matter?4m
Importance Sampling4m
Off-Policy Monte Carlo Prediction5m
Emma Brunskill: Batch Reinforcement Learning12m
Week 1 Summary3m
2개의 읽기 자료
Weekly Reading40m
Chapter Summary40m
1개 연습문제
Graded Quiz30m
3

3

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Temporal Difference Learning Methods for Prediction

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6개 동영상 (총 37분), 1 개의 읽기 자료, 2 개의 테스트
6개의 동영상
Rich Sutton: The Importance of TD Learning6m
The advantages of temporal difference learning5m
Comparing TD and Monte Carlo5m
Andy Barto and Rich Sutton: More on the History of RL12m
Week 2 Summary2m
1개의 읽기 자료
Weekly Reading40m
1개 연습문제
Practice Quiz30m
4

4

완료하는 데 8시간 필요

Temporal Difference Learning Methods for Control

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9개 동영상 (총 30분), 2 개의 읽기 자료, 2 개의 테스트
9개의 동영상
Sarsa in the Windy Grid World3m
What is Q-learning?3m
Q-learning in the Windy Grid World3m
How is Q-learning off-policy?4m
Expected Sarsa3m
Expected Sarsa in the Cliff World3m
Generality of Expected Sarsa1m
Week 3 Summary2m
2개의 읽기 자료
Weekly Reading40m
Chapter summary40m
1개 연습문제
Practice Quiz30m

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강화 학습 특화 과정 정보

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....
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  • 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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