Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

MLPI Lecture 1: Maths for Machine Learning

1,511 views

Published on

This lecture covers several mathematical subjects that are widely used in machine learning, which particularly include general topology, functional analysis, and measure theory.

Published in: Science

MLPI Lecture 1: Maths for Machine Learning

  1. 1. Lecture'1 Maths&for&Machine&Learning Dahua%Lin The$Chinese$University$of$Hong$Kong 1
  2. 2. Roadmap Mathema'cs!lies!at!the!heart!of!machine!learning! research.! Complete(coverage(of(all(these(subjects(is(obviously( beyond(the(scope(of(this(course.(This(lecture(aims(to(give( an(overview(of(several(useful(math(concepts. 2
  3. 3. Concepts)to)Be)Covered • Basics'of'topology • metrics • open'and'closed-sets • con.nuous-func.on • compact-space 3
  4. 4. Concepts)to)Be)Covered)(cont'd) • Basics'of'func,onal'analysis • norm,'inner'product • Banach'space,'Hilbert'space • func5onal,'operator • bilinear'form 4
  5. 5. Concepts)to)Be)Covered)(cont'd) • Basics'of'modern'probability'theory:' • measure'space • Lebesgue'integra0on • random'variables • expecta0on • convergence'of'laws 5
  6. 6. Metrics Measurement*of*distances*or*devia*on*lies*at*the* heart*of*many*learning*problems.* 6
  7. 7. Metrics((Defini-on) • "is"called"a"metric,"if"it"sa-sfies: • (non*nega-vity)" • (coincidence2axiom)" • (symmetry)" • (triangle2inequality)" 7
  8. 8. Metrics((Proper-es) • The%triangle)inequality%can%be%further%generalized: ! • A#set# #together#with#a#metric# #defined#thereon#is# called#a#metric'space,#denoted#by# . 8
  9. 9. Examples)of)Metrics • Euclidean*metric: • Rec$linear*metric: ! 9
  10. 10. Metrics(Induced(by(Transform • One%can%define%a%metric%over%an%arbitrary%set%$S$% through%an%injec&ve(map% %to%a%metric% space% : • It$can$be$easily$verified$that$ $is$a$metric$on$ . 10
  11. 11. Topology'Defined'by'Metrics • Topological)space"is"a"more"general"concept"than" metric)space • With"metrics,"topological)concepts"can"be"defined" in"a"more"intui7ve"way 11
  12. 12. Interior • Open%ball"of"radius" :" • "is"an"interior'point"of" "iff" ." • The"interior"of" ,"denoted"by" "or" ,"is"the" set"of"all"interior"points"of" . 12
  13. 13. Boundary • "is"a"boundary)point"of" ,"iff"for"any" ," " overlaps"with"both" "and" ." • The"boundary"of" ,"denoted"by" ,"is"the"set"of" all"boundary)points"of" . 13
  14. 14. Open%and%Closed%Sets • Consider*a*metric*space* ,*and*a*subset* : • *is*called*an*open%set,*if* • *is*called*a*closed%set,*if* *is*open • *is*open*iff* . • *is*closed*iff* . 14
  15. 15. Proper&es(of(Open(and(Closed(Sets • The%union%of%arbitrary%collec-ons%of%open%sets%is% open. • The%intersec-on%of%finitely+many%open%sets%is%open.% • The%intersec-on%of%arbitrary%collec-ons%of%closed% sets%is%closed. • The%union%of%finitely+many%closed%sets%is%closed. 15
  16. 16. Ques%ons • Consider*an*arbitrary*metric*space* ,*are* *and* *open*or*closed? • Let* *be*a*finite*metric*space*and* ,*is* * open*or*closed? • Consider*Euclidean*space* *and* ,* is* *open*or*closed?*what*is* ? 16
  17. 17. Closure Let$ $be$a$subset$of$a$metric$space$ : • The%closure%of% ,%denoted%by% %or% ,%is%defined% to%be%the%intersec3on%of%all%closed%sets%that%contain% . • %is%closed%if%and%only%if% . • .% • . 17
  18. 18. Closure((Examples) • . • . 18
  19. 19. Convergence • Let% %be%a%sequence%in%a%metric%space% ,%then% %is%called%a%limit%of%this%sequence% if • A#sequence#is#said#to#be#convergent#if#it#has#a#limit. • The#limit#of#a#convergent#sequence#is#unique. 19
  20. 20. Cauchy'Sequence • A#sequence# #is#called#a#Cauchy'sequence#if# ! • A#convergent)sequence#must#be#a#Cauchy)sequence • Ques%on:#Is#a#Cauchy)sequence#always#convergent? 20
  21. 21. Mo#va#ng(Example • Consider*a*real*valued*sequence* • We$have$learned$in$Calculus$that$ $as$ . • Note$that$ $is$a$ra5onal$sequence.$Is$this$ sequence$convergent$in$the$ra#onal'space$ ? 21
  22. 22. Complete(Metric(Space • Convergence)is)not)an)intrinsic)property)of)a)sequence,) which)also)depends)on)the)space)in)which)the) sequence)lies. • A#metric#space# #is#called#a#complete)metric) space#if#every)Cauchy)sequence)converges. • #is#complete,#but# #is#not. • #can#be#constructed#by#comple:ng# . • Ques%on:#Is#the#integer#space# #complete?# 22
  23. 23. Closedness(and(Completeness Let$ $be$a$subset$of$a$complete$metric$space$ : • Being'closed'means'"containing-all-the-limits": • Let' 'be'a'sequence'in' 'and' ,'then' • 'is'closed'if'and'only'if' 'is'complete,'meaning'all- convergent-sequences-in- -converge-within- .' 23
  24. 24. Bounded • The%diameter%of% ,%denoted%by% ,%is%defined%to% be%the%largest%distance%between%points%in% ,%as • "is"said"to"be"bounded"if" . 24
  25. 25. Compactness • In$elementary$Calculus,$we$learned$that$"every& bounded&sequence&in& &has&a&convergent& subsequence".$ • Does$this$hold$in$general$metric$spaces? 25
  26. 26. Compactness+(cont'd) Consider)a)metric)space) : • "is"called"compact"iff"either"of"the"following"holds: • Any"open"cover"of" "has"a"finite"subcover. • "is"complete"and"totally*bounded,"namely,"for" every" ,"there"is"a"finite"cover"of" "by" >balls. • "is"sequen1ally*compact,"namely,"every" sequence"in" "has"a"convergent"subsequence. 26
  27. 27. Compactness+(cont'd) • Compact)sets)are)always)closed'and'bounded. • But,)the)converse)is)generally'false. • )is)compact)if'and'only'if) )is)bounded) and)closed. 27
  28. 28. Con$nuous'Func$on • "is"called"con$nuous,"iff"either"of"the" following"holds:" • (Topological)" "is" open"whenever" "is"open. • (Weierstrass)"Given"any" "and" ," • (Sequen$al3con$nuity)" "whenever" . 28
  29. 29. Con$nui$es)of)Various)Levels • "is"called"uniformly*con,nuous,"if" • "is"called"Lipschitz)con,nuous,"if" ! • Lipschitz)con,nuous" "Uniform)con,nuous" " Con,nuous." 29
  30. 30. Con$nuous'Funcs'on'Compact'Set • Let% %be%a%con+nuous%func+on,%then% %is%compact%whenever% %is% compact. • Let% ,%then% %assumes%maximum%and% minimum%within% %when% %is%compact. • A%con)nuous+func)on%is%uniformly+con)nuous%over%a% compact%domain.%A%con)nuously+differen)able+ func)on%is%Lipschitz+con)nuous%over%a%compact% domain. 30
  31. 31. Dense%Sets • A#subset# #is#said#to#be#dense%in% ,#if# . • A#subset# #is#dense%in% ,#iff#either#of#the#following# holds: • Each#open#subset#of# #contains#at#least#one# element#in# . • Each# #is#a#limit#of#some#sequence#in# . • #is#dense#in# ,# #is#dense#in# . 31
  32. 32. Separable(Space • "is"called"a"separable(space,"if" "contains"a" countable"dense"subset. • Let" "be"a"con3nuous"func3on"on" ,"and" "is" dense,"then" "is"uniquely"determined"by" . • Ques%on:"Let" "be"con3nuous,"and"it"has" "and" ." • Is" "determined"by"these"condi3ons?"If"so,"what" is" ?" 32
  33. 33. Things'become'much'more' interes0ng'when'vector'spaces'meet' with'metrics. 33
  34. 34. Norms • Consider*a*vector'space* ,*a*func0on* * is*called*a*norm*if: • • *iff* * • . • . 34
  35. 35. Banach&Spaces • A#vector#space#together#with#a#norm,#as# ,# is#called#a#normed'space.# • A#normed'space#is#always#a#metric'space,#where# the#norm#induces#a#metric#as# . • A#complete'normed'space#is#called#a#Banach'space. 35
  36. 36. Proper&es(of(Norms • The%norm%func*on% %is%Lipschitz% con*nuous: • The%metric% %induced%by%the%norm%has: • transla'on)invariance:% • 36
  37. 37. !Norm Consider)a)real)vector)space) .)For)each) ,)we) can)define) 6norm)as: It#can#be#easily#verified#that# #is#a#norm.# 37
  38. 38. !Norm When% ,% 'norm%is%s-ll%a%norm,%defined%as We#will#extend#these#norms#to#the#space&of&func+ons. 38
  39. 39. Convergence)of)Vector)Sequences • Let% %be%a%sequence%of%vectors%in%a%Banach% space,%we%say% %converges%to% %if% % as% . • This%is%some9mes%called%convergence+in+norm. 39
  40. 40. Convergence)of)Series • Consider*an*infinite*series* * • Let* .*The*series* *is*said*to*be* convergent*if*the*sequence* *converges. • *is*said*to*be*absolutely1convergent*if*the*series* *converges. • Absolute1convergence*implies*convergence1(in1norm). 40
  41. 41. Basis • In$elementary$linear$algebra,$we$learned$that$a$basis$ of$a$vector$space$is$a$linearly*independent$set$of$ vectors$that$spans$the$space. • If$a$vector$space$has$a$finite$basis,$it$is$called$finite/ dimensional.$ • The$cardinali<es$of$all$bases$of$a$finite/ dimensional$space$are$the$same,$called$the$ dimension$of$the$space. 41
  42. 42. Basis%of%Banach%Space • Let% %be%a%sequence%in%a%Banach%space% ,%it%is% called%a%(Schauder)+basis%of% %if%for%each% ,% there%exists%a%unique%sequence%of%real%values% % such%that% ! • A#Banach#space# #with#a#Schauder#basis#must#be# separable. • Is#the#converse#true?# 42
  43. 43. Basis%of%Banach%Space%(cont'd) • It$took$about$forty$years$to$get$the$answer$33$No.$ • Enflo$constructed$a$separable$Banach$space$with$ no$Schauder$basis$in$1973. 43
  44. 44. Inner%Products • An$inner$product$on$a$real$vector$space$ $is$a$ mapping$ $that$sa5sfies: • • $iff$ • (symmetry)$ • (bilinearity)$ 44
  45. 45. Hilbert(Spaces • A#vector#space#together#with#an#inner#product#is# called#an#inner%product%space. • An#inner%product%space#is#always#a#normed%space,# where#the#norm#is#induced#by#the#inner%product,#as# . • A#complete%inner%product%space#is#called#a#Hilbert% space. • A#Hilbert%space#is#always#a#Banach%space. 45
  46. 46. Euclidean*Space • "is"a"Hilbert(space"with"the"inner"product"defined" by" ,"which"induces"the" 5norm" and"Euclidean(metric. 46
  47. 47. Proper&es(of(Inner(Products • (Parallelogram*equality)" "##"this"only" holds"for"norms"induced"by"inner"products. • (Cauchy–Schwarz*inequality)" ,"or" equivalently," . • (Con9nuity)"if" "and" ,"then" . 47
  48. 48. Orthogonality • "are"said"to"be"orthogonal"to"each"other," denoted"by" ,"if" . • A"subset" "is"called"an"orthogonal)set"if" elements"of" "are"mutually"orthogonal."Moreover," if"each"element"has"a"unit"norm,"then" "is"called" an"orthonormal)set. • (Pythagorean)theorem)" . 48
  49. 49. Projec'on • Let% %and% ,%then%the%distance%between% % and% %is%defined%to%be% . • Let% %be%a%non2empty%convex%closed%subset%of% .% Then%there%exists%a%unique%element% %such%that% .%This%element% %is%called%the% projec/on%of% %onto% ,%denoted%by% . • Ques%on:%Why%does% %need%to%be%closed%and% convex? 49
  50. 50. Projec'on)(cont'd) Let$ $be$a$Hilbert$space$and$ $be$a$closed$subspace: • Given' 'and' ,'then' ,' meaning'that' . Let$ $be$a$projec,on,$where$ $is$non3empty: • • "is"idempotent,"namely," ,"i.e." . 50
  51. 51. Orthogonal*Complement • A#vector#space# #is#called#the#direct'sum#of#two# subspaces# #and# ,#denoted#by# ,#if#each# #can#be#expressed#uniquely#as# #with# #and# .# • The#orthogonal'complement#of# ,#denoted#by# ,#is#defined#to#be# .# • #is#a#closed#subspace#of# . • ,# . 51
  52. 52. Orthonormal*Basis*of*Hilbert*Space • Let% %be%a%Hilbert%space,%a%subset% %is%called%a% total%subset%of% %if% %is%dense. • %is%a%total%subset%of% %if%and%only%if% . • Ques%on:%Why%do%we%only%require% %to% be%dense%instead%of% ? • A%total%orthonormal%set%is%called%an%orthonormal% basis. 52
  53. 53. Orthonormal*Basis*of*Hilbert*Space • (Parseval)theorem)"An"orthonormal"sequence" "is" total"if"and"only"if • In$this$case,$each$ $can$be$uniquely$expressed$ as$ 53
  54. 54. Orthonormal*Basis*of*Hilbert*Space* (cont'd) • Every'Hilbert'space'has'a'total%orthonormal%set.' • Every'separable'Hilbert'space'has'a'total% orthonormal%sequence. 54
  55. 55. Spaces 55
  56. 56. Linear'Operators'and'Func1onals • Let% %and% %be%two%linear%spaces,%a%func5on:% %is%called%a%linear'operator%if%it%preserves% linear'dependency,%that%is,% • In$par(cular,$when$ $is$ ,$ $is$called$a$linear' func+onal. • The$set$ $is$a$subspace$of$ ,$called$the$null'space$of$ . 56
  57. 57. Bounded'Operators Let$ $be$a$linear$operator$on$Banach$spaces: • "is"said"to"be"bounded,"if" . • The"(operator)-norm"of" "is"defined"as ! • "always"holds. 57
  58. 58. Bounded'Operators'(cont'd) • Given'a'linear'operator' ,'the'following'statements' are'equivalent: • 'is'bounded • 'is'con;nuous • 'is'finite' • All'linear'operators'with'finite'dimensional'domain' are'bounded. 58
  59. 59. Space&of&Bounded&Operators • All$bounded$operators$ $form$a$normed) space,$denoted$by$ .$ • When$ $is$a$Banach$space,$ $is$a$Banach$ space. 59
  60. 60. Dual%Spaces • All$bounded$func)onals$ $form$a$normed) space,$called$the$dual)space$of$ ,$denoted$by$ ,$ where$the$norm$is$called$the$dual)norm,$defined$as: • "is"always"a"Banach"space,"no"ma2er"whether" " is"or"not. • "is"generally"not"isomorphic"to"the" . 60
  61. 61. Func%on'Space'and'Uniform'Norm • The%set%of%all%con$nuous%real%valued%func2ons% defined%on%a%compact+space% %forms%a%normed% vector%space,%denoted%by% ,%where%the%norm%is% defined%by% ,%which%is%called%the% uniform+norm%(or%Chebyshev+norm). • When% %is%compact,% %is%a%Banach+space. • There%are%other%ways%to%define%norms%of%func2ons,% which%we%will%discuss%later. 61
  62. 62. Ques%ons • Let% %be%a%subspace%of% %that% comprises%all%con$nuously)differen$able)func$ons%on% .%Let% .%We%define%a%func8onal%as% %which%takes%the%deriva8ve%at% .% • Is% %a%linear%func8onal? • Is% %bounded? 62
  63. 63. Representa)on+of+Func)onals • Every'Hilbert'space'is'isomorphic'to'its'dual%space. • (Riesz's%theorem)'Every'bounded'linear'func8onal' ' on'a'Hilbert'space' 'can'be'represented'in'terms'of' inner'product,'namely,' ,'where' 'is' uniquely'determined'by' 'and'has' .' • Ques%on:'How'can'you'find' 'given' ? 63
  64. 64. Representa)on+of+Func)onals+ (cont'd) • In$a$separable$Hilbert$space,$ $can$be$expressed$as$ a$series: 64
  65. 65. Measure'Theory • Measure'theory"studies"assigning'values'to'subsets. • Measure"theory"is"the"corner"stone"of"many"math" subjects • Modern"approach"to"integra8on"is"based"on" measure"theory. • Modern"probability"theory"is"based"en8rely"on" measure"theory. 65
  66. 66. Lengths(of(Real(Subsets • Intui'vely,-the-measure-of-a-set-can-be-intepreted- as-the-size,-e.g.-the-length-of-an-interval. • How-long-are-these-subsets:- ,- ,-and- ? • What-is-the-length-of- ? • Now-let's-try-to-compute-the-length-of- -by- decomposing0it0into0infinitely0many0points: 66
  67. 67. Lengths(of(Real(Subsets((cont'd) • Sizes'(e.g.'lengths,'areas,'and'volumes)'are'countably- addi0ve. • Let' 'be'a'collec;on'of'subsets'of' ,'then'a' func;on' 'is'said'to'be'countably-addi0ve,' if'for'any'finite'or'countable'sequence'of'disjoint' subsets' ,'it'has 67
  68. 68. The$Vitali$Set • Let's'consider'a'very'interes1ng'subset'of' ,' called'Vitali&set: • We'say' 'and' 'are'equivalent,'if' . • 'can'then'be'par11oned'into'mul1ple' equivalent'classes. • We'have'incountably&infinite'such'classes • By'axiom&of&choices,'we'can'form'a'set' ,'which' contains'one'representa1ve'from'each'class. 68
  69. 69. The$length$of$Vitali$Set • Enumerate*all*ra,onal*numbers*within* *as* ,*and*let* .*Obviously,* *are*disjoint • Let* ,*then* ,*and* therefore* . • However,*if* ,*then* ,*or*if* ,*then* *... 69
  70. 70. Ring%of%Sets • ,#a#nonempty#collec.on#of#subsets#of# ,#is#called#a# ring,#if#it#is#closed#under#union#and#set*difference. • A#ring#must#contain# . • A#ring#is#closed#under#intersec.on. • Overall,#we#have: 70
  71. 71. !algebra • A#ring# #is#called#a#algebra,#if#it#is#also#closed#under# complement. • An#algebra#must#contain# . • An#algebra#is#called#a# 5algebra,#if#it#is#also#closed# under#countable.union. • A# 5algebra#is#closed#under#countable#fold#of#any# elementary#set#opera9ons. 71
  72. 72. Measure'and'Measure'Space • A#countably#addi/ve#func/on# #from#a# 5algebra# # into# #is#called#a#measure.# • The#triple# #is#called#a#measure'space.#All#the# elements#of# #are#called#measurable'sets. 72
  73. 73. Proper&es(of(Measures Consider)a)measure'space) : • . • . • Let& &be&disjoint,&then& . 73
  74. 74. Con$nuity)of)Measures . . 74
  75. 75. Finite&and& )finite&Measures • "is"called"a"finite&measure"if" . • "is"called"a" ,finite&measure"if" "is"covered"by" countably"many"subsets"of"finite"measure. 75
  76. 76. Lengths(of(Open(Intervals • The%length%of%an%open%interval%is%defined%as% . • The%the%length%of%a%finite%union%of%disjoint%open% intervals%is%defined%to%be%the%sum%of%the%lengths%of% individual%intervals. 76
  77. 77. Lengths(of(Open(Intervals((cont'd) • The%collec)on%of%all%finite%unions%of%disjoint%open% intervals%cons)tutes%a%ring. • The%length%is%well%defined%over%this%ring,%and%it% sa)sfies%finite%addi)vity.% • Hence,%it%is%called%a%pre'measure. • We%want%to%extend%the%length%to%as%many%subsets% as%possible. 77
  78. 78. Generated( )algebra • Let% %be%a%collec+on%of%subsets.%Then%the%smallest% 4algebra%that%contains% %is%called%the% !algebra( generated(by( ,%denoted%by% • ,% • countable%intersec+on%of%unions%of%sets%in% %are% in% . 78
  79. 79. Borel& 'algebra • Let% %be%a%metric'space.%The% +algebra%generated%by% the%open%subsets%of% %is%called%the%Borel' .algebra% of% ,%denoted%by% • Elements%of%the%Borel% +algebra%are%called%Borel' sets. • All%the%open%sets,%closed%sets,%and%their%finite%and% countable%unions%and%intersec?ons%are%all%Borel' sets. • Finite%or%countable%sets%are%all%Borel'sets. 79
  80. 80. Measure'Extension • (Hahn–Kolmogorov.theorem):"Let" "be"a"ring"that" covers" ," "be"a"pre4measure,"then" " can"be"extended"to"a"measure" ."If" "is" 9finite,"then"the"extension"is"unique. • The"length"func>on"over"the"open"interval"ring"can" be"uniquely.extended"to"a"measure"over"the"Borel" 9 algebra"over" ,"called"Borel.measure. 80
  81. 81. Complete(Measure(Space We#are#not#done#yet.#Given#a#measure#space# : • A#subset# #is#called#a#null$set#if#it#is#contained# in#a#measurable#set#of#zero#measure. • This#measure$space#is#called#a#complete$measure$ space#if#every#null#set#is#measurable. • Let# #denote#the#collec:on#of#all#the#null$sets.# Then# #is#complete.# 81
  82. 82. Lebesgue'Measure • The%Borel&measure&space%is%generally%not%complete.% • The%comple.on%of%the%Borel&measure&space%is%called% the%Lebesgue&measure&space,%and%the%extended% measure%is%called%Lebesgue&measure. 82
  83. 83. Lebesgue'Integral'.'Indicator' Func4ons To#derive#the#Lebesgue'integral,#we#begin#with#simple# func7ons#and#then#extend#the#defini7on#to#more# general#func7ons.#Let# #be#a#measure#space: • Indicator+func.ons+00+the+integral+of+an+indicator+ func.on+ +of+a+measurable+set+ +is+defined+to+ be+the+measure+of+ ,+as 83
  84. 84. Lebesgue'Integral'.'Simple'Func5ons • Simple(func-ons(00(linear(combina-ons(of(indicator( func-ons: 84
  85. 85. Lebesgue'Integral'.'Nonnega1ve' Func1ons • Let% %be%a%non#nega've%func,on%on% ,%then%we% define 85
  86. 86. Lebesgue'Integral'.'Signed'Func4ons • Signed(func,on:(decompose( (as( ,( with( (and( ,(then • When&both& &and& &are&infinite,& &is&undefined. 86
  87. 87. Proper&es(of(Lebesgue(Integral • "is"a"linear"func-onal. • If" "then" " • a"predicate"holds"almost'everywhere"means"that"it" holds"over" ,"except"for"a"null"set. • (Monotonicity)" . 87
  88. 88. Monotone&Convergence&Theorem Let$ $be$a$sequence$of$non#nega've$func.ons,$and$ $pointwisely,$then$ 88
  89. 89. Dominated*Convergence*Theorem If# #pointwisely,# #is#dominated#by# ,#i.e.# ,#and# ,#then# 89
  90. 90. Integral)with)Coun0ng)Measure • The%coun%ng'measure%over%a%finite%or%countable% space% %is%defined%as%the%cardinality%of%subsets.% • Let% %be%a%coun%ng'measure%over%a%countable%space% ,%then% ! 90
  91. 91. Integral)with)Lebesgue)Measure • Let% %be%a%func,on%over% ,%then%when% %is% Riemann'integrable,% %must%be%Lebesgue'integrable% (w.r.t.%the%Lebesgue'measure),%and%in%such%a%case,%the% Lebesgue'integral%is%equal%to%the%Riemann'integral.% • Ques%on:%Consider% : • Is%it%Riemann'integrable? • Compute%the%Lebesgue'integral%of% %with%respect% to%the%Lebesgue'measure. 91
  92. 92. !space • For%any% ,%the% %space%of% ,%denoted%by% ,%is%the%vector%space%comprised%of%all% measurable%func8ons%on% %such%that% ,%where%the% )norm%is%defined%by • Ques%on:"Is"it"actually"a"norm? 92
  93. 93. !space!(cont'd) • Func&ons)that)are)equal)almost'everywhere)are) indis&nguishable)by)integra&on.)Let) )denotes) the)vector)space)with)all)essen/ally'equivalent) func&ons)merged)into)an)element.) • Then) )is)a)Banach'space)with)the) >norm. • The) )norm)is)defined)as)the)essen/al'supreme)of) ,)as 93
  94. 94. !Space!and!Dual • The%dual%space%of% %is%isomorphic%to% %with% .% • For%each%bounded%func8onal%on% ,%there% exists% :% 94
  95. 95. !Hilbert!Space • "is"a"Hilbert"space,"where"the"inner"product"is" defined"as • The%dual%space%of% %is% . 95
  96. 96. Absolute)Con,nuity Let$ $and$ $be$two$measures$over$ : • "is"said"to"be"absolutely*con-nuous"with"respect"to" ,"denoted"by" ,"if" "whenever" . • "and" "are"said"to"be"singular,"denoted"by" ,"if" there"exists" "such"that" "and" . 96
  97. 97. Lebesgue'Decomposi.on Let$ $and$ $be$two$measures$over$ : There%exists%a%unique%decomposi3on%of% %as% ! such%that% %and% . 97
  98. 98. Radon&Nikodym,Theorem Let$ $be$a$finite$measure$that$is$absolutely*con-nuous$ with$respect$to$ .$Then$there$exists$an$essen-ally$ unique$ :$ Here,% %is%called%the%Radon&Nikodym,density%of% %with% respect%to% .%In%this%case,%we%also%have 98
  99. 99. Event&Spaces • The% &algebra% %can%be%interpreted%as%an%event% space.%Each%element% %is%an%event. • % %both% %and% %happen • % %either% %or% %happens • % % %does%not%happen • % % %and% %are%mutually%exclusive. 99
  100. 100. Probability*Measure • A#probability*measure# #is#a#measure#on# #with# . • #can#be#considered#as#the#probability#of#the# event# . • Obviously,# #sa:sfies#all#the#requirement#of# probability#func:ons#in#classical#probability#theory. 100
  101. 101. Independence • Two%events% %and% %are%said%to%be%independent%if% • We$say$two$collec-ons$of$events$ $and$ $are$ independent$when$ 101
  102. 102. Random'Variables • A#(real&valued)+random+variable# #is#defined#to#be#a# measurable+func4on# . • Each#random#variable# #induces#a#sub7 7algebra,# denoted#by# ,#where# #is#the#Borel# 7algebra# of# . • Two#random#variables# #and# #are#said#to#be# independent#if# #and# #are# independent. 102
  103. 103. Expecta(on • The%expecta'on%of%a%random%variable% %is%defined% to%be ! • The%expecta'on%is%a%bounded-linear-func'onal. 103
  104. 104. Probability*and*Law • The%probability%that%the%value%of% %falling%in% %is% then%given%by% ." • The%func*on% ,%which%maps%each%Borel%set% %to%a%probability*value,%is%itself%a%probability* measure%over% ,%which%is%called%the%law%of% ,% denoted%by% . 104
  105. 105. Probability*Density • The%Radon+Nikodym%density%of% %with%respect% to%the%Lebesgue%measure%over% ,%denoted%by% ,%is% called%the%probability*density%of% .%So%we%have% . 105
  106. 106. (Seman'c)*Correspondence • "algebra) )event)space • sub" "algebra) )a)family)of)events • measure) ) )probability)func6on • measurable)func6on) ) )random)variable • ) )law)of) • integra6on) )expecta6on • Radon"Nikodym)density) )probability)density 106
  107. 107. Convex'Analysis • Convex(op*miza*on(plays(a(crucial(role(in(many( machine(learning(problems • Prof.(Stephen'Boyd(has(a(very(famous(book("Convex' Op1miza1on",(which(provides(an(excellent( treatment(of(this(subject • The(PDF(version(is(available(for(download(in( Prof.(Boyd's(website • You(should(at(least(read(the(first(five(chapters 107
  108. 108. Convex'Analysis'(cont'd) • Important*concepts • convex'set • convex'func,on • conjugate'func,on • Lagrange'dual • strong'duality'and'weak'duality • We*will*begin*to*use*these*concepts*in*Lecture*3. 108

×