Uncertainty
Artificial Intelligence
| Beginner
- 13 videos | 39m 30s
- Includes Assessment
- Earns a Badge
Many problems aren't fully observable and have some degree of uncertainty, which is challenging for AI to solve. Discover how to make agents deal with uncertainty and make the best decisions.
WHAT YOU WILL LEARN
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Describe uncertainty and how it applies to ai
Describe how probability theory is used to represent knowledge to help an intelligent make decisions
Describe utility theory and how an agent can calculate expected utility of decisions
Describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents
Describe how risks are taken into consideration when calculating utility and how attitude for risks can change the utility function
Describe the utility of information gain and how information gain can influence decisions
Define markov chains -
Define the markov decision process and how it applies to ai
Describe the value iteration algorithm to decide on an optimal policy for a markov decision process
Define the partially observable markov decision process and contrast it with a regular markov decision process
Describe how the value iteration algorithm is used with the partially observable markov decision process
Describe how a partially observable markov decision process can be implemented with an intelligent agent
Describe the markov decision process and how it can be used by an intelligent agent
IN THIS COURSE
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3m 29sUpon completion of this video, you will be able to describe uncertainty and how it applies to artificial intelligence. FREE ACCESS
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5m 7sAfter completing this video, you will be able to describe how probability theory is used to represent knowledge to help an intelligent agent make decisions. FREE ACCESS
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3. Utility Theory1m 18sUpon completion of this video, you will be able to describe utility theory and how an agent can calculate expected utility of decisions. FREE ACCESS
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4. Utility and Preferences3m 9sUpon completion of this video, you will be able to describe how preferences are involved in decision making and how the same problem can have different utility functions with different agents. FREE ACCESS
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5. Utility and Risks3m 45sAfter completing this video, you will be able to describe how risks are taken into consideration when calculating utility and how attitude towards risks can change the utility function. FREE ACCESS
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6. Value of Information2m 29sAfter completing this video, you will be able to describe the usefulness of information gain and how information gain can influence decisions. FREE ACCESS
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7. Markov Chains3m 20sFind out how to define Markov chains. FREE ACCESS
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8. Markov Decision Process2m 27sIn this video, you will learn about the Markov Decision Process and how it applies to AI. FREE ACCESS
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9. MDP Value Iteration2m 29sUpon completion of this video, you will be able to describe the value iteration algorithm and how it can be used to decide on an optimal policy for a Markov Decision Process. FREE ACCESS
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10. Partially Observable Markov Decision Process (POMDP)2m 46sIn this video, you will learn how to define the partially observable Markov Decision Process and how it differs from a regular Markov Decision Process. FREE ACCESS
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11. POMDP Value Iteration3m 30sAfter completing this video, you will be able to describe how the value iteration algorithm is used with the partially observable Markov Decision Process. FREE ACCESS
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12. Applying POMDPs3m 7sUpon completion of this video, you will be able to describe how a partially observable Markov Decision Process can be implemented with an intelligent agent. FREE ACCESS
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13. Exercise: Describe the Markov Decision Process2m 35sUpon completion of this video, you will be able to describe the Markov Decision Process and how it can be used by an intelligent agent. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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