Sign in to an API client such as Graph Explorer, Postman, or create your own client app to call Microsoft Graph.A working Azure AD tenant with an Azure AD Premium P2 or EMS E5 license enabled.To complete this tutorial, you need the following resources and privileges: This test environment saves you time by helping you properly define and validate your queries without repeatedly recompiling your application. You can use Graph Explorer or Postman to try out and test your access reviews API calls before you automate them into a script or an app.
This tutorial guides you to use the access reviews API to review access to a security group in your Azure AD tenant. Periodically, you need to attest that all members of the security group need their membership and by extension, their access to the resources assigned to the security group. Suppose you use Azure AD security groups to assign identities (also called principals) access to resources in your organization. Using the access reviews API, organizations can periodically attest to principals that have access to such groups and by extension, other resources in the organization. For example, hundreds of users can be assigned to a security group and the security group assigned access to a folder. One of the most efficient and effective methods to manage access privileges for principals to other resources is through Azure AD security groups. Machine learning is a powerful set of techniques that allow computers to learn from data rather than having a human expert program a behavior by hand.The access reviews API in Microsoft Graph enables organizations to audit and attest to the access that identities (also called principals) are assigned to resources in the organization. Neural networks are a class of machine learning algorithm originally inspired by the brain, but which have recently have seen a lot of success at practical applications. They're at the heart of production systems at companies like Google and Facebook for face recognition, speech-to-text, and language understanding. This course gives an overview of both the foundational ideas and the recent advances in neural net algorithms. Roughly the first 2/3 of the course focuses on supervised learning - training the network to produce a specified behavior when one has lots of labeled examples of that behavior. The last 1/3 focuses on unsupervised learning and reinforcement learning. We will use Piazza for the course forum.Please do not contact us at our personal emails.Instructors and head TA only: csc421instructors cs.Since both sections are fully subscribed, please attend the one you are registered for.
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