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-------------------课程目录-------------------(U)A'Y4b5G8Q
2A0M3I5{#B$F(U$Z
  01_handout.pdf7q&A7g9I6u-]5Y(}
  02_handout.pdf6H5{.k5?  C:Y![-u)@:_
  03_handout.pdf
  04_handout.pdf,A+j.}  P.o+d2|
  05_handout.pdf
  06_handout.pdf
  07_handout.pdf
  08_handout.pdf4l%U$a"~!u)R5W4P;s*T"i
  09_handout.pdf/M5B&O0m6B!N.N
  1-1-CourseIntroduction(10-58).mp4
  1-2-WhatisMachineLearning(18-28).mp4&J!d7K"E+J.k7B
  1-3-ApplicationsofMachineLearning(18-56).mp4
  1-4-ComponentsofMachineLearning(11-45).mp4
  1-5-MachineLearningandOtherFields(10-21).mp40o$l:s9D:B3n
  10-1-LogisticRegressionProblem(14-33).mp4
  10-2-LogisticRegressionError(15-58).mp4
  10-3-GradientofLogisticRegressionError(15-38).mp4
  10-4-GradientDescent(19-18).mp44u*J"P*Q$j'`
  10_handout.pdf8i&V.f:s(b0z#Q6U
  11-1-LinearModelsforBinaryClassification(21-35).mp4
  11-2-StochasticGradientDescent(11-39).mp4
  11-3-MulticlassviaLogisticRegression(14-18).mp4:\9X1v!h5q4]5t6c8J,v
  11-4-MulticlassviaBinaryClassification(11-35).mp4
  11_handout.pdf0L*x5E-i,_%V1Q
  12-1-QuadraticHypothesis(23-47).mp4+u  B,X8Y-C4?9j7n$v)Q-j9B
  12-2-NonlinearTransform(09-52).mp4
  12-3-PriceofNonlinearTransform(15-37).mp4
  12-4-StructuredHypothesisSets(09-36).mp4
  12_handout.pdf
  2-1-PerceptronHypothesisSet(15-42).mp4
  2-2-PerceptronLearningAlgorithm(PLA)(19-46).mp4
  2-3-GuaranteeofPLA(12-37).mp41a)Y*|/G5m&m
  2-4-Non-SeparableData(12-55).mp4/f)}  y/{&c6n2j!m
  3-1-LearningwithDifferentOutputSpace(17-26).mp4$Y4?;q,{!t"T4B+U
  3-2-LearningwithDifferentDataLabel(18-12).mp4:m6c2r!o;Y/G6B
  3-3-LearningwithDifferentProtocol(11-09).mp4
  3-4-LearningwithDifferentInputSpace(14-13).mp4
  4-1-LearningisImpossible-(13-32).mp4$b8y#b"E;k3m
  4-2-ProbabilitytotheRescue(11-33).mp4
  4-3-ConnectiontoLearning(16-46).mp45j.v.R4R7U%@6?
  4-4-ConnectiontoRealLearning(18-06).mp4&I"`(m4s*F9P
  5-1-RecapandPreview(13-44).mp4$t/}!m7y6M%o
  5-2-EffectiveNumberofLines(15-26).mp4
  5-3-EffectiveNumberofHypotheses(16-17).mp4
  5-4-BreakPoint(07-44).mp4
  6-1-RestrictionofBreakPoint(14-18).mp4
  6-2-BoundingFunction-BasicCases(06-56).mp4
  6-3-BoundingFunction-InductiveCases(14-47).mp4
  6-4-APictorialProof(16-01).mp4-F;g8m5i+S2D:H
  7-1-DefinitionofVCDimension(13-10).mp4
  7-2-VCDimensionofPerceptrons(13-27).mp4
  7-3-PhysicalIntuitionofVCDimension(6-11).mp4  M5R+E2B4x8V"B5E9]
  7-4-InterpretingVCDimension(17-13).mp4*e.a%x+l#y*R/w7h&P-V
  8-1-NoiseandProbabilisticTarget(17-01).mp4
  8-2-ErrorMeasure(15-10).mp4$F:|  |'@!O,u9g:E7d1f
  8-3-AlgorithmicErrorMeasure(13-46).mp4-e#E"Y5[5Z&p+w8T
  8-4-WeightedClassification(16-54).mp40f0r*~3O,r.]%J%~7`-|
  9-1-LinearRegressionProblem(10-08).mp4
  9-2-LinearRegressionAlgorithm(20-03).mp4
  9-3-GeneralizationIssue(20-34).mp4+c)V9V.A!A.f'N
  9-4-LinearRegressionforBinaryClassification(11-23).mp4
  HomeWork1.doc
  homework2.docx
  homework3.docx

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