Acerca de este Curso
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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. 20 horas para completar

Sugerido: 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Los estudiantes que toman este Course son
  • Data Scientists
  • Machine Learning Engineers
  • Scientists
  • Researchers
  • Research Assistants

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. 20 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!

2 videos (Total 10 minutos), 2 lecturas
2 videos
Meet your instructors!8m
2 lecturas
Reinforcement Learning Textbook10m
Read Me: Pre-requisites and Learning Objectives10m
Semana
2
3 horas para completar

Monte Carlo Methods for Prediction & Control

11 videos (Total 58 minutos), 2 lecturas, 1 cuestionario
11 videos
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
2 lecturas
Weekly Reading40m
Chapter Summary40m
1 ejercicio de práctica
Graded Quiz
Semana
3
6 horas para completar

Temporal Difference Learning Methods for Prediction

6 videos (Total 37 minutos), 1 lectura, 2 cuestionarios
6 videos
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
1 lectura
Weekly Reading40m
1 ejercicio de práctica
Practice Quiz30m
Semana
4
8 horas para completar

Temporal Difference Learning Methods for Control

9 videos (Total 30 minutos), 2 lecturas, 2 cuestionarios
9 videos
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
2 lecturas
Weekly Reading40m
Chapter summary40m
1 ejercicio de práctica
Practice Quiz18m
4.8
32 revisionesChevron Right

Principales revisiones sobre Sample-based Learning Methods

por KNOct 3rd 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

por IFSep 29th 2019

Great course. Clear, concise, practical. Right amount of programming. Right amount of tests of conceptual knowledge. Almost perfect course.

Instructores

Avatar

Martha White

Assistant Professor
Computing Science
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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 de 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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