What is reinforcement learning, and why is it important to learn about?

Reinforcement learning is a machine learning paradigm in which software agents use a process of trial and error to learn how to complete tasks in a way that maximizes cumulative rewards as defined by their programmers. In contrast to supervised learning paradigms, reinforcement learning systems do not need labeled input/output pairs or explicit corrections of suboptimal actions; and, in contrast to unsupervised learning, reinforcement learning defines an explicit goal, which is the maximization of the value returned by the Q-learning (or “quality” learning) algorithm as a result of its actions. Because it combines the goal orientation of supervised learning with the flexibility of unsupervised learning, reinforcement learning is very important in creating artificial intelligence (AI) applications requiring successful problem-solving in complex situations. For example, they are often used in financial engineering to develop optimal trading algorithms for the stock market. They are also used to build intelligent systems to allow robots and self-driving cars to navigate real-world environments safely.

What kinds of careers can I have with a background in reinforcement learning?

As one of the main paradigms for machine learning, reinforcement learning is an essential skill for careers in this fast-growing field. Reinforcement learning is particularly important for developing artificially intelligent digital agents for real-world problem-solving in industries like finance, automotive, robotics, logistics, and smart assistants. According to Glassdoor , the average annual salary for machine learning engineers in America is $114,121 per year, a high level of pay which reflects the high level of demand for this expertise.

Can I learn about reinforcement learning by taking online courses on Coursera?

Absolutely. Coursera hosts a wide variety of courses in reinforcement learning and related topics in machine learning, as well as the use of these techniques in applied contexts such as finance and self-driving cars. These courses and Specializations are offered by top-ranked institutions in this field, including the deepmind.ai, New York University, the University of Toronto, and the University of Alberta’s Machine Intelligence Institute. You can learn remotely on a flexible schedule while still getting feedback from expert professors and instructors, ensuring that you’ll get a high quality education with all the reinforcement you need to learn these valuable skills with confidence.

What skills or experience do I need to already have before starting to learn reinforcement learning?

Because reinforcement learning itself isn't a beginner-level subject, you'll need to have a good grasp on the fundamentals of machine learning before starting to learn it. Additionally, many courses will require you to have a strong background in high-level mathematics such as linear algebra, statistics, and probability. Most courses will require you to be proficient in Python, although people familiar with other programming languages like C++, Matlab, and JavaScript can often use those skills to help them learn reinforcement learning. Having the ability to implement algorithms from pseudocode may be another prerequisite. As you progress, you'll gain skills in using reinforcement learning solutions to solve problems with probabilistic artificial intelligence, function approximation, and intelligent systems.

What kind of people are best suited for roles in reinforcement learning?

People best suited to roles within the reinforcement learning realm should have a passion for machine learning with a drive for analytics and data and an interest in providing frontline support to solve real-world problems while leveraging innate creative problem-solving skills. Additionally, many companies like to see that candidates have strong communication skills and the ability to collaborate across disciplines and departments. There are a variety of roles associated with reinforcement learning, including analysts, engineers, and researchers. In late February 2021, there were more than 1,800 job listings for people proficient in reinforcement learning on LinkedIn.

How do I know if learning reinforcement learning is right for me?

If you want to be a part of the future of machine learning, learning reinforcement learning may be a good move for you. This innovative machine learning technique creates an algorithm that learns through trial and error, leading to a combination of short- and long-term rewards such as the ability to define sequences to solve problems using a reward-based learning approach. It's useful across multiple industries, including the tech industry, business, advertising, finance, and e-commerce, all of which find reinforcement learning useful in part because of its ability to offer greater personalization. Ultimately, if you want to work within AI and machine learning, this could be a step to advancing your goals.

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Reinforcement Learning

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"reinforcement learning"에 대한 84개의 결과

University of Colorado Boulder
Spacecraft Dynamics and Control
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4.8
(381개의 검토)
Advanced · Specialization · 3-6 Months
University of Colorado Boulder
Master of Science in Data Science
획득할 기술: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Mathematics, Strategy and Operations, Computer Architecture, Databases, Leadership and Management, Communication, Hardware Design, Statistical Programming, Operating Systems, Machine Learning, Research and Design, Data Analysis, Finance, Computer Programming, SQL, Writing, Regression, Business Analysis, Data Structures, Project Management, Software Engineering, Business Communication, Probability Distribution, Entrepreneurship, Database Design, Statistical Tests, Computer Graphics, Computer Networking, Design and Product, Statistical Analysis, Accounting, Systems Design, Human Computer Interaction, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Statistical Machine Learning, PostgreSQL, Estimation, Data Visualization, Problem Solving, Operations Research, Internet Of Things, Network Architecture, Applied Mathematics, Computer Vision, Graph Theory, Deep Learning, Geometry, User Experience, Marketing, Computer Graphic Techniques, Mathematical Theory & Analysis, Programming Principles, Python Programming, Supply Chain and Logistics, Algebra, Cryptography, Business Psychology, Interactive Design, Critical Thinking, Security Engineering, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, Linux, User Experience Design, Cost Accounting, Differential Equations, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Scrum (Software Development), Applied Machine Learning, Budget Management, Calculus, Econometrics, Experiment, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Investment Management, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Human Resources, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Network Model, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, C Programming Language Family, Contract Management, Data Architecture, FinTech, Financial Accounting, Financial Management, Geovisualization, Inventory Management, Management Accounting, Markov Model, Matlab, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Leadership Development, Network Analysis, Network Security, System Software
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University of Colorado Boulder
Master of Engineering in Engineering Management
획득할 기술: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Mathematics, Strategy and Operations, Computer Architecture, Databases, Leadership and Management, Communication, Hardware Design, Statistical Programming, Operating Systems, Machine Learning, Research and Design, Data Analysis, Finance, Computer Programming, SQL, Writing, Regression, Business Analysis, Data Structures, Project Management, Software Engineering, Business Communication, Probability Distribution, Entrepreneurship, Database Design, Statistical Tests, Computer Graphics, Computer Networking, Design and Product, Statistical Analysis, Accounting, Systems Design, Human Computer Interaction, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Statistical Machine Learning, PostgreSQL, Estimation, Data Visualization, Problem Solving, Operations Research, Internet Of Things, Network Architecture, Applied Mathematics, Computer Vision, Graph Theory, Deep Learning, Geometry, User Experience, Marketing, Computer Graphic Techniques, Mathematical Theory & Analysis, Programming Principles, Python Programming, Supply Chain and Logistics, Algebra, Cryptography, Business Psychology, Interactive Design, Critical Thinking, Security Engineering, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, Linux, User Experience Design, Cost Accounting, Differential Equations, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Scrum (Software Development), Applied Machine Learning, Budget Management, Calculus, Econometrics, Experiment, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Investment Management, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Human Resources, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Network Model, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, C Programming Language Family, Contract Management, Data Architecture, FinTech, Financial Accounting, Financial Management, Geovisualization, Inventory Management, Management Accounting, Markov Model, Matlab, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Leadership Development, Network Analysis, Network Security, System Software
학위 취득
Degree · 1-4 Years
University of Colorado Boulder
Master of Science in Electrical Engineering
획득할 기술: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Mathematics, Strategy and Operations, Computer Architecture, Databases, Leadership and Management, Communication, Hardware Design, Statistical Programming, Operating Systems, Machine Learning, Research and Design, Data Analysis, Finance, Computer Programming, SQL, Writing, Regression, Business Analysis, Data Structures, Project Management, Software Engineering, Business Communication, Probability Distribution, Entrepreneurship, Database Design, Statistical Tests, Computer Graphics, Computer Networking, Design and Product, Statistical Analysis, Accounting, Systems Design, Human Computer Interaction, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Statistical Machine Learning, PostgreSQL, Estimation, Data Visualization, Problem Solving, Operations Research, Internet Of Things, Network Architecture, Applied Mathematics, Computer Vision, Graph Theory, Deep Learning, Geometry, User Experience, Marketing, Computer Graphic Techniques, Mathematical Theory & Analysis, Programming Principles, Python Programming, Supply Chain and Logistics, Algebra, Cryptography, Business Psychology, Interactive Design, Critical Thinking, Security Engineering, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, Linux, User Experience Design, Cost Accounting, Differential Equations, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Scrum (Software Development), Applied Machine Learning, Budget Management, Calculus, Econometrics, Experiment, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Investment Management, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Human Resources, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Network Model, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, C Programming Language Family, Contract Management, Data Architecture, FinTech, Financial Accounting, Financial Management, Geovisualization, Inventory Management, Management Accounting, Markov Model, Matlab, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Leadership Development, Network Analysis, Network Security, System Software
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무료
University of Illinois at Urbana-Champaign
IoT Devices
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4.7
(106개의 검토)
Intermediate · Course · 1-4 Weeks

reinforcement learning과(와) 관련된 검색

reinforcement learning in finance

reinforcement learning for trading strategies

reinforcement learning: qwik start

a complete reinforcement learning system (capstone)

fundamentals of reinforcement learning

unsupervised learning, recommenders, reinforcement learning

machine learning and reinforcement learning in finance

deep learning and reinforcement learning

요약하자면, 여기에 가장 인기 있는 reinforcement learning 강좌 10개가 있습니다.

An Introduction to Practical Deep Learning: Intel
Master of Science in Machine Learning and Data Science: Imperial College London
Spacecraft Dynamics and Control: University of Colorado Boulder
Master of Science in Data Science: University of Colorado Boulder
Detect and Mitigate Ethical Risks: CertNexus
Master of Engineering in Engineering Management: University of Colorado Boulder
Master of Science in Electrical Engineering: University of Colorado Boulder
Everyday Parenting: The ABCs of Child Rearing: Yale University
Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership: SAS
Build Basic Generative Adversarial Networks (GANs): DeepLearning.AI

Machine Learning에서 학습할 수 있는 스킬

강화 학습에 대한 자주 묻는 질문

Reinforcement learning is a machine learning paradigm in which software agents use a process of trial and error to learn how to complete tasks in a way that maximizes cumulative rewards as defined by their programmers. In contrast to supervised learning paradigms, reinforcement learning systems do not need labeled input/output pairs or explicit corrections of suboptimal actions; and, in contrast to unsupervised learning, reinforcement learning defines an explicit goal, which is the maximization of the value returned by the Q-learning (or “quality” learning) algorithm as a result of its actions.
Because it combines the goal orientation of supervised learning with the flexibility of unsupervised learning, reinforcement learning is very important in creating artificial intelligence (AI) applications requiring successful problem-solving in complex situations. For example, they are often used in financial engineering to develop optimal trading algorithms for the stock market. They are also used to build intelligent systems to allow robots and self-driving cars to navigate real-world environments safely.‎
As one of the main paradigms for machine learning, reinforcement learning is an essential skill for careers in this fast-growing field. Reinforcement learning is particularly important for developing artificially intelligent digital agents for real-world problem-solving in industries like finance, automotive, robotics, logistics, and smart assistants. According to Glassdoor, the average annual salary for machine learning engineers in America is $114,121 per year, a high level of pay which reflects the high level of demand for this expertise.‎
Absolutely. Coursera hosts a wide variety of courses in reinforcement learning and related topics in machine learning, as well as the use of these techniques in applied contexts such as finance and self-driving cars. These courses and Specializations are offered by top-ranked institutions in this field, including the deepmind.ai, New York University, the University of Toronto, and the University of Alberta’s Machine Intelligence Institute. You can learn remotely on a flexible schedule while still getting feedback from expert professors and instructors, ensuring that you’ll get a high quality education with all the reinforcement you need to learn these valuable skills with confidence.‎
Because reinforcement learning itself isn't a beginner-level subject, you'll need to have a good grasp on the fundamentals of machine learning before starting to learn it. Additionally, many courses will require you to have a strong background in high-level mathematics such as linear algebra, statistics, and probability. Most courses will require you to be proficient in Python, although people familiar with other programming languages like C++, Matlab, and JavaScript can often use those skills to help them learn reinforcement learning. Having the ability to implement algorithms from pseudocode may be another prerequisite. As you progress, you'll gain skills in using reinforcement learning solutions to solve problems with probabilistic artificial intelligence, function approximation, and intelligent systems.‎
People best suited to roles within the reinforcement learning realm should have a passion for machine learning with a drive for analytics and data and an interest in providing frontline support to solve real-world problems while leveraging innate creative problem-solving skills. Additionally, many companies like to see that candidates have strong communication skills and the ability to collaborate across disciplines and departments. There are a variety of roles associated with reinforcement learning, including analysts, engineers, and researchers. In late February 2021, there were more than 1,800 job listings for people proficient in reinforcement learning on LinkedIn.‎
If you want to be a part of the future of machine learning, learning reinforcement learning may be a good move for you. This innovative machine learning technique creates an algorithm that learns through trial and error, leading to a combination of short- and long-term rewards such as the ability to define sequences to solve problems using a reward-based learning approach. It's useful across multiple industries, including the tech industry, business, advertising, finance, and e-commerce, all of which find reinforcement learning useful in part because of its ability to offer greater personalization. Ultimately, if you want to work within AI and machine learning, this could be a step to advancing your goals.‎