Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data.
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Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data.
Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data.
The course is aimed at those working with data, this includes both those charged with analyzing the data and those in charge of making decisions based on that data.
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Syllabus
Wald and Sequential Decisions
This module introduces the class and the approach to teaching it to be used for the next five weeks. We begin with simple sequential data, similar to Wald’s model: data arrives from a distribution and is not time dependent. This can be generative data. We then explore increasingly complex data from distributions collected for health or business reasons. We finish the week with connections to code work and to AI.
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Syllabus
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Activities
Review Probability and Linear Algebra
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Refreshing your knowledge of probability and linear algebra will provide a solid foundation for understanding the concepts in this course.
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Probability
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-
Review your notes or textbooks on probability and linear algebra.
-
Solve practice problems to test your understanding.
-
Seek help from a tutor or mentor if needed.
Curate Resources on Markov Processes
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Compiling resources related to Markov Processes will provide you with a valuable collection for future reference and deeper exploration.
Browse courses on
Markov Processes
Show steps
-
Search for online resources on Markov Processes, including articles, tutorials, and videos.
-
Organize the resources into a structured format, such as a document or spreadsheet.
-
Annotate each resource with a brief description and your evaluation of its quality.
Review Pre-Calculus
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Review the trigonometric identities and properties of functions.
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Pre-Calculus
Show steps
-
Revisit unit circle and special triangle ratios.
-
Refresh properties of sine, cosine, and tangent functions.
-
Examine the graphs of trigonometric functions and their transformations.
-
Practice solving trigonometric equations.
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Industry Expert Mentorship
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Connect with experienced professionals to gain insights into real-world applications and best practices in sequential decision-making.
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Machine Learning
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-
Identify potential mentors in relevant fields
-
Reach out and schedule informational interviews
-
Seek guidance on career development and technical challenges
Discuss Sequential Decision Making
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Engaging in discussions with peers will help you clarify your understanding of sequential decision making and gain new perspectives.
Show steps
-
Find a study partner or join a study group.
-
Choose a topic related to sequential decision making.
-
Discuss your ideas and insights with your partner or group.
-
Work together to solve problems and answer questions.
Collaborative Study Groups
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Enhance understanding and critical thinking by discussing concepts, solving problems, and sharing perspectives with peers.
Browse courses on
Markov Processes
Show steps
-
Form a study group with 2-4 other students
-
Set regular meeting times and goals
-
Take turns presenting concepts, leading discussions, and solving problems
Explore Markov Chain Tutorials
Show steps
Following this series of tutorials will help provide a conceptual understanding of how Markov Chains work and how they can be applied to real-world scenarios.
Browse courses on
Markov Chains
Show steps
-
Identify introductory Markov Chain tutorials with high ratings.
-
Follow the instructions and work through the examples.
-
Apply the concepts you learned to solve practice problems.
-
Document the key concepts and techniques you learned.
Code Challenges
Show steps
Reinforce theoretical concepts and improve algorithmic thinking by solving challenging programming problems.
Show steps
-
Identify key concepts and algorithms
-
Break down the problem into smaller steps
-
Implement solutions and test correctness
Build a Markov Chain Model
Show steps
Building a Markov Chain model will provide you with hands-on experience and a deeper understanding of how they work.
Browse courses on
Markov Chains
Show steps
-
Identify a real-world scenario that can be modeled using a Markov Chain.
-
Create a state transition matrix for your model.
-
Implement the model in a programming language.
-
Analyze the output of your model and draw conclusions.
Interactive Simulations
Show steps
Develop a deeper understanding of complex concepts by creating interactive simulations that illustrate their behavior.
Browse courses on
Markov Chains
Show steps
-
Choose a specific concept or model to simulate
-
Design the simulation interface and logic
-
Implement the simulation using appropriate software tools
Data Analysis Project
Show steps
Apply sequential decision-making principles to real-world data and present findings in a well-structured report.
Browse courses on
Data Analytics
Show steps
-
Identify a suitable dataset and problem statement
-
Develop a decision-making model and algorithm
-
Analyze data and evaluate results
Solve Markov Decision Process Problems
Show steps
Solving Markov Decision Process problems will strengthen your understanding of the concepts and improve your problem-solving skills.
Browse courses on
Markov Decision Processes
Show steps
-
Find a collection of Markov Decision Process practice problems.
-
Solve the problems using the concepts you learned in the course.
-
Analyze your solutions and identify areas for improvement.
-
Document the key concepts and techniques you used.
Study 'Reinforcement Learning: An Introduction'
Show steps
This book provides a comprehensive overview of reinforcement learning, which builds on the concepts of Markov Decision Processes.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read the book thoroughly, taking notes on key concepts.
-
Solve the exercises and practice problems provided in the book.
-
Discuss the book's concepts with peers or a mentor.
Contribute to an Open-Source Markov Chain Library
Show steps
Contributing to an open-source Markov Chain library will provide you with practical experience and strengthen your understanding of the underlying algorithms.
Browse courses on
Markov Chains
Show steps
-
Identify a reputable open-source Markov Chain library.
-
Review the library's documentation and codebase.
-
Identify an area where you can contribute, such as adding a new feature or improving the documentation.
-
Submit a pull request with your changes.
Review Probability and Linear Algebra
Show steps
Refreshing your knowledge of probability and linear algebra will provide a solid foundation for understanding the concepts in this course.
Browse courses on
Probability
Show steps
- Review your notes or textbooks on probability and linear algebra.
- Solve practice problems to test your understanding.
- Seek help from a tutor or mentor if needed.
Curate Resources on Markov Processes
Show steps
Compiling resources related to Markov Processes will provide you with a valuable collection for future reference and deeper exploration.
Browse courses on
Markov Processes
Show steps
- Search for online resources on Markov Processes, including articles, tutorials, and videos.
- Organize the resources into a structured format, such as a document or spreadsheet.
- Annotate each resource with a brief description and your evaluation of its quality.
Review Pre-Calculus
Show steps
Review the trigonometric identities and properties of functions.
Browse courses on
Pre-Calculus
Show steps
- Revisit unit circle and special triangle ratios.
- Refresh properties of sine, cosine, and tangent functions.
- Examine the graphs of trigonometric functions and their transformations.
- Practice solving trigonometric equations.
Industry Expert Mentorship
Show steps
Connect with experienced professionals to gain insights into real-world applications and best practices in sequential decision-making.
Browse courses on
Machine Learning
Show steps
- Identify potential mentors in relevant fields
- Reach out and schedule informational interviews
- Seek guidance on career development and technical challenges
Discuss Sequential Decision Making
Show steps
Engaging in discussions with peers will help you clarify your understanding of sequential decision making and gain new perspectives.
Show steps
- Find a study partner or join a study group.
- Choose a topic related to sequential decision making.
- Discuss your ideas and insights with your partner or group.
- Work together to solve problems and answer questions.
Collaborative Study Groups
Show steps
Enhance understanding and critical thinking by discussing concepts, solving problems, and sharing perspectives with peers.
Browse courses on
Markov Processes
Show steps
- Form a study group with 2-4 other students
- Set regular meeting times and goals
- Take turns presenting concepts, leading discussions, and solving problems
Explore Markov Chain Tutorials
Show steps
Following this series of tutorials will help provide a conceptual understanding of how Markov Chains work and how they can be applied to real-world scenarios.
Browse courses on
Markov Chains
Show steps
- Identify introductory Markov Chain tutorials with high ratings.
- Follow the instructions and work through the examples.
- Apply the concepts you learned to solve practice problems.
- Document the key concepts and techniques you learned.
Code Challenges
Show steps
Reinforce theoretical concepts and improve algorithmic thinking by solving challenging programming problems.
Show steps
- Identify key concepts and algorithms
- Break down the problem into smaller steps
- Implement solutions and test correctness
Build a Markov Chain Model
Show steps
Building a Markov Chain model will provide you with hands-on experience and a deeper understanding of how they work.
Browse courses on
Markov Chains
Show steps
- Identify a real-world scenario that can be modeled using a Markov Chain.
- Create a state transition matrix for your model.
- Implement the model in a programming language.
- Analyze the output of your model and draw conclusions.
Interactive Simulations
Show steps
Develop a deeper understanding of complex concepts by creating interactive simulations that illustrate their behavior.
Browse courses on
Markov Chains
Show steps
- Choose a specific concept or model to simulate
- Design the simulation interface and logic
- Implement the simulation using appropriate software tools
Data Analysis Project
Show steps
Apply sequential decision-making principles to real-world data and present findings in a well-structured report.
Browse courses on
Data Analytics
Show steps
- Identify a suitable dataset and problem statement
- Develop a decision-making model and algorithm
- Analyze data and evaluate results
Solve Markov Decision Process Problems
Show steps
Solving Markov Decision Process problems will strengthen your understanding of the concepts and improve your problem-solving skills.
Browse courses on
Markov Decision Processes
Show steps
- Find a collection of Markov Decision Process practice problems.
- Solve the problems using the concepts you learned in the course.
- Analyze your solutions and identify areas for improvement.
- Document the key concepts and techniques you used.
Study 'Reinforcement Learning: An Introduction'
Show steps
This book provides a comprehensive overview of reinforcement learning, which builds on the concepts of Markov Decision Processes.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
- Read the book thoroughly, taking notes on key concepts.
- Solve the exercises and practice problems provided in the book.
- Discuss the book's concepts with peers or a mentor.
Contribute to an Open-Source Markov Chain Library
Show steps
Contributing to an open-source Markov Chain library will provide you with practical experience and strengthen your understanding of the underlying algorithms.
Browse courses on
Markov Chains
Show steps
- Identify a reputable open-source Markov Chain library.
- Review the library's documentation and codebase.
- Identify an area where you can contribute, such as adding a new feature or improving the documentation.
- Submit a pull request with your changes.
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