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Aaron Brown
Gain insights into environment dynamics, uncertainty reduction, and expectation maximization. Learn about world models' role in AI and their biological and neuroscience connections.
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What's inside
Syllabus
Explore World Models: predicting future observations, self-supervised learning, agent interaction, biological inspiration, generative capabilities, and their applications.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops self-supervised learning, uncertainty reduction, agent interaction, and generative capabilities, which are core skills for AI development
Builds a foundation for learners who are new to world models and their applications
Affirms connections to biology and neuroscience, bridging the gap between AI and natural systems
Requires Udacity account for access, which may introduce barriers for some learners
Save this course
Save Discovering World Models to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Discovering World Models with these
activities:
Review Basic Statistics Concepts
Show steps
Refresh your knowledge of basic statistics and probability to strengthen your understanding of the role of uncertainty in world models.
Browse courses on
Statistics
Show steps
-
Review core statistical concepts (mean, variance, probability distributions)
-
Practice solving probability problems
Review Reinforcement Learning Fundamentals
Show steps
Recall the fundamental concepts of reinforcement learning to enhance your understanding of how world models can be used for decision-making.
Browse courses on
Reinforcement Learning
Show steps
-
Revisit reinforcement learning algorithms (e.g., Q-learning, SARSA)
-
Review the concept of reward functions and value estimation
Review Probability and Statistics concepts
Show steps
Brush up on fundamental probability and statistics concepts to strengthen your foundation for the course.
Browse courses on
Probability
Show steps
-
Review key probability distributions (e.g., normal, binomial, Poisson)
-
Go through basic statistical concepts (e.g., mean, variance, hypothesis testing)
-
Solve practice problems to test your understanding
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Compile a Resource Library on World Models
Show steps
Organize and collect a comprehensive list of resources (articles, videos, tutorials) on world models to supplement your learning.
Show steps
-
Search for relevant resources
-
Create a centralized repository
-
Categorize and annotate the resources
Participate in study groups
Show steps
Engage with peers to clarify concepts, discuss ideas, and enhance your understanding.
Show steps
-
Join or form a study group with other course participants
-
Meet regularly to discuss course material, solve problems, and share insights
-
Provide support and encouragement to fellow group members
Review: Artificial Intelligence: A Modern Approach (4th Edition)
Show steps
Get your hands on a reference text to provide additional depth and help you understand the fundamentals of AI.
View
Artificial Intelligence
on Amazon
Show steps
-
Read chapters 1-3
-
Work on exercises and assignments
-
Engage in discussion forums
Attend Weekly Study Group
Show steps
Engage in regular discussions with peers to enhance your understanding of the course material and gain diverse perspectives.
Show steps
-
Review the week's lecture notes and assignments
-
Attend the weekly study group
-
Participate actively in discussions
Explore PyTorch tutorials
Show steps
Enhance your understanding by following guided tutorials on PyTorch, a popular deep learning framework.
Browse courses on
PyTorch
Show steps
-
Go through the official PyTorch tutorial
-
Complete hands-on exercises to apply your knowledge
-
Explore additional tutorials on specific PyTorch topics
Follow Udacity's Deep Learning Nanodegree Program
Show steps
Supplement your learning with guided tutorials and exercises from Udacity's Deep Learning program, deepening your understanding of the foundational concepts.
Browse courses on
Deep Learning
Show steps
-
Complete the Introduction to Deep Learning course
-
Work on the Convolutional Neural Networks course
Solve World Model-Related Coding Challenges
Show steps
Sharpen your coding skills and reinforce your understanding of world models by solving coding challenges on platforms like LeetCode or HackerRank.
Show steps
-
Identify relevant coding challenges
-
Solve the challenges
-
Review your solutions
Solve Bayesian inference exercises
Show steps
Solidify your understanding of Bayesian inference by tackling practice exercises.
Browse courses on
Bayesian Inference
Show steps
-
Work through textbook exercises on Bayesian inference
-
Use online resources to find and solve additional exercises
-
Collaborate with peers to discuss and solve complex problems
Project: Implement a World Model in Python
Show steps
Build a project where you implement a world model in Python, and apply the techniques learned in the course.
Show steps
-
Design the world model
-
Implement the model in Python
-
Train and evaluate the model
Develop a project to demonstrate world models
Show steps
Apply your learning by creating a project that showcases the concepts of world models.
Show steps
-
Define a problem statement and design a world model
-
Implement the world model and evaluate its performance
-
Write a report documenting your project and findings
Review Basic Statistics Concepts
Show steps
Refresh your knowledge of basic statistics and probability to strengthen your understanding of the role of uncertainty in world models.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review core statistical concepts (mean, variance, probability distributions)
- Practice solving probability problems
Review Reinforcement Learning Fundamentals
Show steps
Recall the fundamental concepts of reinforcement learning to enhance your understanding of how world models can be used for decision-making.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Revisit reinforcement learning algorithms (e.g., Q-learning, SARSA)
- Review the concept of reward functions and value estimation
Review Probability and Statistics concepts
Show steps
Brush up on fundamental probability and statistics concepts to strengthen your foundation for the course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review key probability distributions (e.g., normal, binomial, Poisson)
- Go through basic statistical concepts (e.g., mean, variance, hypothesis testing)
- Solve practice problems to test your understanding
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Compile a Resource Library on World Models
Show steps
Organize and collect a comprehensive list of resources (articles, videos, tutorials) on world models to supplement your learning.
Show steps
Show steps
Show steps
- Search for relevant resources
- Create a centralized repository
- Categorize and annotate the resources
Participate in study groups
Show steps
Engage with peers to clarify concepts, discuss ideas, and enhance your understanding.
Show steps
Show steps
Show steps
- Join or form a study group with other course participants
- Meet regularly to discuss course material, solve problems, and share insights
- Provide support and encouragement to fellow group members
Review: Artificial Intelligence: A Modern Approach (4th Edition)
Show steps
Get your hands on a reference text to provide additional depth and help you understand the fundamentals of AI.
View
Artificial Intelligence
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Work on exercises and assignments
- Engage in discussion forums
Attend Weekly Study Group
Show steps
Engage in regular discussions with peers to enhance your understanding of the course material and gain diverse perspectives.
Show steps
Show steps
Show steps
- Review the week's lecture notes and assignments
- Attend the weekly study group
- Participate actively in discussions
Explore PyTorch tutorials
Show steps
Enhance your understanding by following guided tutorials on PyTorch, a popular deep learning framework.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Go through the official PyTorch tutorial
- Complete hands-on exercises to apply your knowledge
- Explore additional tutorials on specific PyTorch topics
Follow Udacity's Deep Learning Nanodegree Program
Show steps
Supplement your learning with guided tutorials and exercises from Udacity's Deep Learning program, deepening your understanding of the foundational concepts.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Complete the Introduction to Deep Learning course
- Work on the Convolutional Neural Networks course
Solve World Model-Related Coding Challenges
Show steps
Sharpen your coding skills and reinforce your understanding of world models by solving coding challenges on platforms like LeetCode or HackerRank.
Show steps
Show steps
Show steps
- Identify relevant coding challenges
- Solve the challenges
- Review your solutions
Solve Bayesian inference exercises
Show steps
Solidify your understanding of Bayesian inference by tackling practice exercises.
Browse courses on
Bayesian Inference
Show steps
Show steps
Show steps
- Work through textbook exercises on Bayesian inference
- Use online resources to find and solve additional exercises
- Collaborate with peers to discuss and solve complex problems
Project: Implement a World Model in Python
Show steps
Build a project where you implement a world model in Python, and apply the techniques learned in the course.
Show steps
Show steps
Show steps
- Design the world model
- Implement the model in Python
- Train and evaluate the model
Develop a project to demonstrate world models
Show steps
Apply your learning by creating a project that showcases the concepts of world models.
Show steps
Show steps
Show steps
- Define a problem statement and design a world model
- Implement the world model and evaluate its performance
- Write a report documenting your project and findings
Career center
Learners who complete Discovering World Models will develop knowledge and skills
that may be useful to these careers:
Computational Neuroscientist
Computational Neuroscientist
A Computational Neuroscientist uses computational models to understand the brain. Given that this course covers topics such as biological and neuroscience connections, it would likely provide a strong foundation for a Computational Neuroscientist.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer develops and builds artificial intelligence systems. Given that this course covers topics such as world models and their applications, it would likely provide a strong foundation for an Artificial Intelligence Engineer.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data. Given that this course covers topics such as uncertainty reduction and expectation maximization, it may be helpful for those who would be working with data that is either uncertain or missing.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and builds machine learning models. Given that this course covers topics such as predictive modeling, this course may be helpful for those who would be applying ML to a variety of projects.
Software Engineer
Software Engineer
A Software Engineer designs and builds software. Given that this course covers topics such as self-supervised learning and agent interaction, this course may be useful for those who would be working on developing software that is self-learning or that interacts with other agents.
Systems Engineer
Systems Engineer
A Systems Engineer designs and builds systems. Given that this course covers topics such as environment dynamics and uncertainty reduction, this course may be useful for those who would be working on developing systems that are resilient to uncertainty.
Technical Evangelist
Technical Evangelist
A Technical Evangelist promotes and educates people about new technologies. Given that this course covers topics such as world models and their applications, this course may be useful for those who would be educating people about new AI technologies.
Technical Writer
Technical Writer
A Technical Writer writes technical documentation. Given that this course covers topics such as world models and their applications, this course may be useful for those who would be writing documentation for complex technical systems.
Biomedical Engineer
Biomedical Engineer
A Biomedical Engineer develops and builds medical devices. Given that this course covers topics such as biological and neuroscience connections, this course may be useful for those who might be working on developing new medical devices.
Aerospace Engineer
Aerospace Engineer
An Aerospace Engineer designs and builds aircraft and spacecraft. Given that this course covers topics such as environment dynamics, it may be useful for those who might want to apply these skills to the development of aircraft and spacecraft.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs and builds robots. Given that this course covers topics such as self-supervised learning and predicting future observations, it may be useful to those who might want to apply these skills to the development of robotics.
Product Manager
Product Manager
A Product Manager manages the development and launch of products. Given that this course covers topics such as world models and their applications, this course may be useful for those who would be working on developing new products.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. Given that this course covers topics such as environment dynamics, it may be useful for those who would be working on solving business problems that are related to the environment.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and develops solutions to improve them. Given that this course covers topics such as environment dynamics, this course may be useful for those who would be working on analyzing business processes that are affected by the environment.
Data Scientist
Data Scientist
A Data Scientist works with large amounts of data to develop models. Given that this course covers topics such as uncertainty reduction and expectation maximization, it may be helpful for those who would be working with data that is either uncertain or missing.
For more career information including salaries, visit:
OpenCourser.com/course/78vf2h/discovering
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Discovering World Models.
Covers foundational concepts in Bayesian statistics, including estimation, hypothesis testing, and model building. It valuable resource for gaining a deeper understanding of the statistical principles underlying world models.
This classic textbook provides a comprehensive overview of reinforcement learning, which is essential for understanding how world models are used to interact with the environment.
This textbook provides a rigorous treatment of probabilistic machine learning, including Bayesian inference and graphical models, which are essential for understanding world models.
Provides a practical guide to deep learning using Python, including topics such as CNNs, RNNs, and GANs, which are relevant for understanding world models.
Save
This textbook provides a comprehensive overview of Bayesian reasoning and machine learning, which are essential for understanding world models.
This textbook provides a comprehensive overview of machine learning, covering a wide range of topics, including supervised and unsupervised learning, which are relevant for world models.
Save
Provides a comprehensive overview of deep learning, including topics such as CNNs, RNNs, and GANs, which are relevant for understanding world models.
Provides a comprehensive overview of machine learning for data science, covering a wide range of topics, including supervised and unsupervised learning, which are relevant for world models.
This classic textbook provides a comprehensive overview of reinforcement learning, which is essential for understanding how world models are used to interact with the environment.
Provides a practical guide to deep learning using Python, including topics such as CNNs, RNNs, and GANs, which are relevant for understanding world models.
Provides a comprehensive overview of deep learning, including topics such as CNNs, RNNs, and GANs, which are relevant for understanding world models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/78vf2h/discovering
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Effort
1 hour
Via
Udacity
Instructor
Aaron Brown
Language
English
Good to know
Develops self-supervised learning, uncertainty reduction, agent interaction, and generative capabilities, which are core skills for AI development
Builds a foundation for learners who are new to world models and their applications
Affirms connections to biology and neuroscience, bridging the gap between AI and natural systems
Requires Udacity account for access, which may introduce barriers for some learners
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