Acerca de este Curso
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100 % en línea

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Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

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

Aprox. 14 horas para completar

Sugerido: 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Formalize problems as Markov Decision Processes

  • Check

    Understand basic exploration methods and the exploration / exploitation tradeoff

  • Check

    Understand value functions, as a general-purpose tool for optimal decision-making

  • Check

    Know how to implement dynamic programming as an efficient solution approach to an industrial control problem

Habilidades que obtendrás

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

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

Aprox. 14 horas para completar

Sugerido: 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
1 hora para completar

Welcome to the Course!

4 videos (Total 20 minutos), 2 readings
4 videos
Course Introduction5m
Meet your instructors!8m
Your Specialization Roadmap3m
2 lecturas
Reinforcement Learning Textbook10m
Read Me: Pre-requisites and Learning Objectives10m
7 horas para completar

The K-Armed Bandit Problem

8 videos (Total 46 minutos), 3 readings, 2 quizzes
8 videos
Learning Action Values4m
Estimating Action Values Incrementally5m
What is the trade-off?7m
Optimistic Initial Values6m
Upper-Confidence Bound (UCB) Action Selection5m
Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8m
Week 1 Summary3m
3 lecturas
Module 2 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 ejercicio de práctica
Exploration/Exploitation45m
Semana
2
4 horas para completar

Markov Decision Processes

6 videos (Total 24 minutos), 2 readings, 2 quizzes
6 videos
Examples of MDPs4m
The Goal of Reinforcement Learning3m
Continuing Tasks5m
Examples of Episodic and Continuing Tasks3m
Week 2 Summary1m
2 lecturas
Module 3 Learning Objectives10m
Weekly Reading30m
1 ejercicio de práctica
MDPs45m
Semana
3
3 horas para completar

Value Functions & Bellman Equations

8 videos (Total 48 minutos), 3 readings, 2 quizzes
8 videos
Value Functions6m
Bellman Equation Derivation6m
Why Bellman Equations?5m
Optimal Policies7m
Optimal Value Functions5m
Using Optimal Value Functions to Get Optimal Policies8m
Week 3 Summary4m
3 lecturas
Module 4 Learning Objectives10m
Weekly Reading30m
Chapter Summary13m
2 ejercicios de práctica
Value Functions and Bellman Equations45m
Value Functions and Bellman Equations45m
Semana
4
6 horas para completar

Dynamic Programming

8 videos (Total 42 minutos), 3 readings, 2 quizzes
8 videos
Iterative Policy Evaluation8m
Policy Improvement4m
Policy Iteration8m
Flexibility of the Policy Iteration Framework4m
Efficiency of Dynamic Programming5m
Week 4 Summary2m
Congratulations!3m
3 lecturas
Module 5 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 ejercicio de práctica
Dynamic Programming45m
4.8
20 revisionesChevron Right

Principales revisiones sobre Fundamentals of Reinforcement Learning

por NSAug 4th 2019

The ideal course to go with the book Reinforcement Learning: An Introduction. The quizzes and coding workshops are pitched just right in my opinion, neither too easy nor too hard.

por ATJul 31st 2019

Very clear and engaging presentation, well thought out and typical Coursera-style programming assignments. Definitely looking forward to taking the rest of the sequence.

Instructores

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Martha White

Assistant Professor
Computing Science
Avatar

Adam White

Assistant Professor
Computing Science

Acerca de Universidad de Alberta

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....

Acerca de 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....

Acerca del programa especializado Aprendizaje por refuerzo

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....
Aprendizaje por refuerzo

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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