ECE 595ML Lecture 13.1: Connecting Bayesian with Linear Regression - Linear Regression Review

By Stanley H. Chan

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Researchers should cite this work as follows:

  • Stanley H. Chan (2020), "ECE 595ML Lecture 13.1: Connecting Bayesian with Linear Regression - Linear Regression Review," https://nanohub.org/resources/32591.

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WTHR 200, Purdue University, West Lafayette, IN

Tags

  1. Bayesian
  2. Bayesian statistics
  3. linear regression
ECE 595ML Lecture 13.1: Connecting Bayesian with Linear Regression - Linear Regression Review
  • Lecture 13.1: Connecting Bayesian with Linear Regression - Review 1. Lecture 13.1: Connecting Bayes… 0
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  • Overview 2. Overview 58.558558558558559
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  • Outline 3. Outline 109.00900900900902
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  • Linear Regression Reviewed 4. Linear Regression Reviewed 176.60994327660995
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  • Geometry of Linear Regression 5. Geometry of Linear Regression 336.67000333667
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  • Loss Function 6. Loss Function 541.77510844177516
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  • Solution of Linear Regression 7. Solution of Linear Regression 596.96363029696363
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  • When ATA is large 8. When ATA is large 613.7804471137805
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  • Treating Linear Regression as Maximum-Likelihood 9. Treating Linear Regression as … 852.28561895228563
    00:00/00:00
  • Treating Linear Regression as Maximum-a-Posteriori 10. Treating Linear Regression as … 1137.0704037370704
    00:00/00:00
  • Ridge Regression 11. Ridge Regression 1260.06006006006
    00:00/00:00
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