Midterm the Since Topics ÷ based Instance and classification ] learning NN . regression Kernel . mgohssien ... ] ' , margin than Peru " learning , . .←dy←u,¢ online approaches trick kernel kernel based ^ , SVM . K means . - ] unsupervised Learning PCA . GMM e for GMM EM . optimal Classifier structural models Bayes . ] Bayes Naive ' ' Nets Bayes . learning Deny . ÷ N nearest neighbors Predict of K . using avg . . a a € • . . : =L distance functions Possible Euclidean . Manhattan - other .
tradeoff K ? function of Bids 1 Variance as discontinuities Note has : KNN regression . ke-ssiacldsfhdt.tn ' ' nearest H ' ' neighbors s . function Given Kernel kx , xq ) i&Vkt ( ×i yc . , I = , i#xx ' dsl bandwidth Kernel d : b. variance exyf.lt#lh) ( ) kernel K : Gaussian ' ' = i. xq , Boxcar kernel others ,
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nt § , ,L(w , xi ) : Todd C # Minimize njn = year - . + - yx D= %LCx , |¥w4w,xl=d ← ohfinud ) nlcx a- Batch : hinge min 't "l 't it ,t{ - ' , .l±c]%.xi{ Illydwct w 7 .x P=tnasq)forh loss :#
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