Bayesian network - Caltech

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Announcements ! Exam: ! Take home ! December 8 10am )ll December 9 10am ! Open book and lecture notes ! No other materials, no collabora)on ! Recita)on next Thursday ! Final project due December 7 2
Page 4 and 5: Linear regression: squared loss
Page 6 and 7: Regulariza)on ≈ Posterior infe
Page 8 and 9: ! Predict according to 0-‐1
Page 10 and 11: Logis)c regression ! Maximize (co
Page 12 and 13: Reminder: Bayesian networks ! A
Page 15 and 16: Maximum likelihood es)ma)on 15
Page 17 and 18: MLE for general Bayes nets ! Gi
Page 19 and 20: Regularizing Bayesian Networks !
Page 21 and 22: Beta prior over parameters ! Bet
Page 23 and 24: Learning parameters for dynamica
Page 25 and 26: Score based structure learning !
Page 27 and 28: Example: Likelihood score E J A B
Page 29 and 30: Finding the op)mal MLE structur
Page 31 and 32: Finding the best structure ! Fin
Page 33 and 34: Finding the op)mal tree ! Given
Page 35 and 36: Example E B A M J 0 0 0 0 0

Bayesian network - Caltech

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