Bayesian network - Caltech

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Regulariza)on ≈ Posterior inference ! A priori, assume weights should be small need fewer bits to describe, simpler model 6
Classifica)on ! In classifica)on, want to predict discrete label ! For example: binary linear classifica)on Spam Ham x 2 ++++++++--------x 1 f(x;w) = sign(w 0 + w 1 x 1 + w 2 x 2 ) 7
Page 2: Announcements ! Exam: ! Take home
Page 5: Overfibng ! Min. training error
Page 9 and 10: Logis)c regression ! Key idea: P
Page 11 and 12: Summary supervised learning ! Wan
Page 13: Next topic: Learning BN from D
Page 16 and 17: Es)ma)ng CPDs P(F=cherry)θP(F=che
Page 18 and 19: ! Given: Algorithm for Bayes net
Page 20 and 21: Pseudo-‐counts ! Make prior a
Page 22 and 23: Bayesian parameter es)ma)on ΘFla
Page 24 and 25: Structure learning ! So far: Dis
Page 26 and 27: MLE score ! Idea: For fixed str
Page 28 and 29: Maximizing the MLE score ! Score
Page 30 and 31: ! Prefer “simpler” models Reg
Page 32 and 33: Finding the op)mal tree BN ! Sc
Page 34 and 35: Chow-‐Liu algorithm ! For eac
Page 36: Summary ! To learn a Bayes net,

Bayesian network - Caltech

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