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Fundamentals of Reinforcement Learning

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University of Alberta

via

Coursera

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Course Report - Fundamentals of Reinforcement Learning

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

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Course Features

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Duration

15 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

15 hours

Course Description

Reinforcement Learning is a subfield within Machine Learning but it is also a general-purpose formalism for automated decision making and AI. This course will introduce you to statistical learning techniques that allow agents to take explicit actions and interact with the world. With more companies becoming interested in interactive agents and intelligent decisions-making, it is vital to understand the challenges and importance of learning agents that make decision.

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Highlights

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Pedagogy

Top 30 Percentile

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Hands on training

Top 30 Percentile

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Parameters

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Pedagogy

Acquire all major Decentralized Application skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Decentralized Application, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

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Hands on training

Inclusion of the capstone project and hands-on training offers a dual focus. This enriches its value, making it an ideal choice for those seeking comprehensive learning and real-world application in Decentralized Application.

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Course Overview

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Formalize problems as markov decision processes

Understand basic exploration methods and the exploration / exploitation tradeoff

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

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

Course Instructors

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Martha White

Computing Science

Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science. Her research focus is on developing algorithms for agents continually ...
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Adam White

Computing Science

Adam White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science, and a Senior Research Scientist at DeepMind. Adam's research focuses on ...
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Course Reviews

Average Rating Based on 4 reviews

4.8

75%

25%

Course FAQs

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Specialization?

Is financial aid available?

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