Short Overview: For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: In this video I spend a little but of time talking about some theoretical concepts in

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This video is part of the Introduction to Machine Learning (I2ML) course from the SLDS teaching program at LMU Munich. Definitions; decision boundary; separability; using nonlinear features.

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In this video I spend a little but of time talking about some theoretical concepts in For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: For more information about Stanford's online Artificial Intelligence programs visit: This lecture covers:

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  • In this video I spend a little but of time talking about some theoretical concepts in
  • For more information about Stanford's Artificial Intelligence professional and graduate programs, visit:
  • Definitions; decision boundary; separability; using nonlinear features.
  • For more information about Stanford's online Artificial Intelligence programs visit: This lecture covers:
  • This video is part of the Introduction to Machine Learning (I2ML) course from the SLDS teaching program at LMU Munich.

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Linear classifiers (1): Basics

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For more information about Stanford's Artificial Intelligence professional and graduate programs, visit:

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Read more details and related context about Linear Classification: Understanding the Fundamentals and Theory.

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Read more details and related context about Linear Classifier.

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In this video I spend a little but of time talking about some theoretical concepts in

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I2ML - 03 Supervised Classification - 03 Linear Classifiers

This video is part of the Introduction to Machine Learning (I2ML) course from the SLDS teaching program at LMU Munich.