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%%EndComments

%%BeginIdrawPrologue
/arrowhead {
0 begin
transform originalCTM itransform
/taily exch def
/tailx exch def
transform originalCTM itransform
/tipy exch def
/tipx exch def
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/dx tipx tailx sub def
/angle dx 0 ne dy 0 ne or { dy dx atan } { 90 } ifelse def
gsave
originalCTM setmatrix
tipx tipy translate
angle rotate
newpath
arrowHeight neg arrowWidth 2 div moveto
0 0 lineto
arrowHeight neg arrowWidth 2 div neg lineto
patternNone not {
originalCTM setmatrix
/padtip arrowHeight 2 exp 0.25 arrowWidth 2 exp mul add sqrt brushWidth mul
arrowWidth div def
/padtail brushWidth 2 div def
tipx tipy translate
angle rotate
padtip 0 translate
arrowHeight padtip add padtail add arrowHeight div dup scale
arrowheadpath
ifill
} if
brushNone not {
originalCTM setmatrix
tipx tipy translate
angle rotate
arrowheadpath
istroke
} if
grestore
end
} dup 0 9 dict put def

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newpath
arrowHeight neg arrowWidth 2 div moveto
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arrowHeight neg arrowWidth 2 div neg lineto
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0 begin
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y exch get /tipy exch def
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brushLeftArrow { tipx tipy tailx taily arrowhead } if
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%%EndIdrawPrologue

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/brushWidth idef
false /brushNone idef
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1 1 n 3 sub {
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n 3 sub dup n 2 sub dup n 1 sub dup 2 copy false subspline
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n 2 sub dup n 1 sub dup 2 copy 2 copy false subspline
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brushNone not { istroke } if
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n 3 sub dup n 2 sub dup n 1 sub dup 0 0 false subspline
n 2 sub dup n 1 sub dup 0 0 1 1 false subspline
patternNone not { ifill } if
brushNone not { istroke } if
} {
Poly
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} dup 0 4 dict put def

/Elli {
0 begin
newpath
4 2 roll
translate
scale
0 0 1 0 360 arc
patternNone not { ifill } if
brushNone not { istroke } if
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} dup 0 1 dict put def

/Line {
0 begin
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x 0 get y 0 get moveto
x 1 get y 1 get lineto
brushNone not { istroke } if
0 0 1 1 leftarrow
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} dup 0 4 dict put def

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n 1 gt {
x 0 get y 0 get moveto
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/Poly {
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patternNone not { ifill } if
brushNone not { istroke } if
} def

/Rect {
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l b moveto
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r t lineto
r b lineto
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} dup 0 4 dict put def

/Text {
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/idef {
dup where { pop pop pop } { exch def } ifelse
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eoclip
originalCTM setmatrix
pathbbox /t exch def /r exch def /b exch def /l exch def
/w r l sub ceiling cvi def
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/imageByteWidth w 8 div ceiling cvi def
/imageHeight h def
bgred bggreen bgblue setrgbcolor
eofill
fgred fggreen fgblue setrgbcolor
w 0 gt h 0 gt and {
l w add b translate w neg h scale
w h true [w 0 0 h neg 0 h] { patternproc } imagemask
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/istroke {
gsave
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[] 0 setdash
1 setgray
} {
brushDashArray brushDashOffset setdash
fgred fggreen fgblue setrgbcolor
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brushWidth setlinewidth
originalCTM setmatrix
stroke
grestore
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/ishow {
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gsave
fgred fggreen fgblue setrgbcolor
/fontDict printFont printSize scalefont dup setfont def
/descender fontDict begin 0 [FontBBox] 1 get FontMatrix end
transform exch pop def
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0 vertoffset moveto show
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} forall
grestore
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} dup 0 3 dict put def
/patternproc {
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/patternHeight patternByteLength 8 mul sqrt cvi def
/patternWidth patternHeight def
/patternByteWidth patternWidth 8 idiv def
/imageByteMaxLength imageByteWidth imageHeight mul
stringLimit patternByteWidth sub min def
/imageMaxHeight imageByteMaxLength imageByteWidth idiv patternHeight idiv
patternHeight mul patternHeight max def
/imageHeight imageHeight imageMaxHeight sub store
/imageString imageByteWidth imageMaxHeight mul patternByteWidth add string def
0 1 imageMaxHeight 1 sub {
/y exch def
/patternRow y patternByteWidth mul patternByteLength mod def
/patternRowString patternString patternRow patternByteWidth getinterval def
/imageRow y imageByteWidth mul def
0 patternByteWidth imageByteWidth 1 sub {
/x exch def
imageString imageRow x add patternRowString putinterval
} for
} for
imageString
end
} dup 0 12 dict put def

/min {
dup 3 2 roll dup 4 3 roll lt { exch } if pop
} def

/max {
dup 3 2 roll dup 4 3 roll gt { exch } if pop
} def

/midpoint {
0 begin
/y1 exch def
/x1 exch def
/y0 exch def
/x0 exch def
x0 x1 add 2 div
y0 y1 add 2 div
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} dup 0 4 dict put def

/thirdpoint {
0 begin
/y1 exch def
/x1 exch def
/y0 exch def
/x0 exch def
x0 2 mul x1 add 3 div
y0 2 mul y1 add 3 div
end
} dup 0 4 dict put def

/subspline {
0 begin
/movetoNeeded exch def
y exch get /y3 exch def
x exch get /x3 exch def
y exch get /y2 exch def
x exch get /x2 exch def
y exch get /y1 exch def
x exch get /x1 exch def
y exch get /y0 exch def
x exch get /x0 exch def
x1 y1 x2 y2 thirdpoint
/p1y exch def
/p1x exch def
x2 y2 x1 y1 thirdpoint
/p2y exch def
/p2x exch def
x1 y1 x0 y0 thirdpoint
p1x p1y midpoint
/p0y exch def
/p0x exch def
x2 y2 x3 y3 thirdpoint
p2x p2y midpoint
/p3y exch def
/p3x exch def
movetoNeeded { p0x p0y moveto } if
p1x p1y p2x p2y p3x p3y curveto
end
} dup 0 17 dict put def

/storexyn {
/n exch def
/y n array def
/x n array def
n 1 sub -1 0 {
/i exch def
y i 3 2 roll put
x i 3 2 roll put
} for
} def

/SSten {
fgred fggreen fgblue setrgbcolor
dup true exch 1 0 0 -1 0 6 -1 roll matrix astore
} def

/FSten {
dup 3 -1 roll dup 4 1 roll exch
newpath
0 0 moveto
dup 0 exch lineto
exch dup 3 1 roll exch lineto
0 lineto
closepath
bgred bggreen bgblue setrgbcolor
eofill
SSten
} def

/Rast {
exch dup 3 1 roll 1 0 0 -1 0 6 -1 roll matrix astore
} def

%%EndProlog

%I Idraw 10 Grid 8 8 

%%Page: 1 1

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%I b u
%I cfg u
%I cbg u
%I f u
%I p u
%I t
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/originalCTM matrix currentmatrix def

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%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
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Times-Roman 12 SetF
%I t
[ 0.978079 -0.208233 0.208233 0.978079 308.911 588.629 ] concat
%I
[
(methods to measure and maximize generalization)
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[ 0.945865 -0.324561 0.324561 0.945865 41.6674 611.985 ] concat
%I
[
(Hebb's rule and PCA)
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.948683 0.316228 -0.316228 0.948683 127.646 551.599 ] concat
%I
[
(Q-learning)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
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[ 0.994916 -0.100712 0.100712 0.994916 292.383 331.497 ] concat
%I
[
(generalized expectation/maximization)
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%I t
[ 0.975403 -0.22043 0.22043 0.975403 359.083 224.63 ] concat
%I
[
(perceptrons and linear programming)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.9792 0.202897 -0.202897 0.9792 457.353 290.555 ] concat
%I
[
(multilayer perceptrons)
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Begin %I Text
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%I t
[ 0.96245 -0.27146 0.27146 0.96245 69.3181 44.1433 ] concat
%I
[
(infomax)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.963915 0.26621 -0.26621 0.963915 146.763 307.185 ] concat
%I
[
(recurrent neural networks)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.932571 -0.360988 0.360988 0.932571 484.978 678.975 ] concat
%I
[
(biological neurons)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.555666 0.831406 -0.831406 0.555666 147.225 582.899 ] concat
%I
[
(sensory processing)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.256308 0.966595 -0.966595 0.256308 535.215 395.579 ] concat
%I
[
(lateral competition)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.0350318 0.999386 -0.999386 0.0350318 594.923 396.229 ] concat
%I
[
(trajectory learning in continuous-time dynamical systems)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.798316 0.602239 -0.602239 0.798316 470.81 219.735 ] concat
%I
[
(the structure of the Hessian)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.940266 0.340441 -0.340441 0.940266 387.252 598.164 ] concat
%I
[
(Gaussian mixture model)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.939947 0.34132 -0.34132 0.939947 295.439 409.454 ] concat
%I
[
(the method of maximum entropy)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.969015 -0.247004 0.247004 0.969015 229.599 461.72 ] concat
%I
[
(Helmholtz machines)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.839075 -0.544016 0.544016 0.839075 64.7051 93.6891 ] concat
%I
[
(Boltzmann machines)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.99153 -0.129881 0.129881 0.99153 88.9388 228.395 ] concat
%I
[
(simulated annealing)
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End

Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ -4.37114e-08 -1 1 -4.37114e-08 578.5 46.5 ] concat
%I
[
(ARP)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.916399 0.400266 -0.400266 0.916399 245.286 212.053 ] concat
%I
[
(attractor networks)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.973841 0.22723 -0.22723 0.973841 392.876 660.578 ] concat
%I
[
(hidden units)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.973575 0.228369 -0.228369 0.973575 237.157 544.645 ] concat
%I
[
(hidden structure)
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Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.987299 -0.158876 0.158876 0.987299 347.182 769.281 ] concat
%I
[
(feature detectors)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.964931 -0.262503 0.262503 0.964931 50.1015 281.216 ] concat
%I
[
(Bayesian interpretation)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.9961 0.0882261 -0.0882261 0.9961 436.877 753.779 ] concat
%I
[
(probabilistic framework)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.964764 -0.263117 0.263117 0.964764 439.026 590.594 ] concat
%I
[
(nonlinear compression)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.887994 -0.459854 0.459854 0.887994 499.256 614.578 ] concat
%I
[
(autoencoder)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.993994 0.109431 -0.109431 0.993994 182.384 415.121 ] concat
%I
[
(associative networks)
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End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.998821 -0.048539 0.048539 0.998821 159.291 83.9998 ] concat
%I
[
(Vapnic-Chervonenkis dimension)
] Text
End

Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.98917 0.146777 -0.146777 0.98917 370.252 83.5815 ] concat
%I
[
(probably approximately correct)
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Begin %I Text
%I cfg DkGray
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ -0.127752 -0.991806 -0.991806 0.127752 551.526 99.1587 ] concat
%I
[
(broken symmetry)
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Begin %I Text
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[
(stochastic gradient methods)
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[ 0.992011 0.126152 -0.126152 0.992011 444.186 331.056 ] concat
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[
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[ 0.998818 -0.0486091 0.0486091 0.998818 265.788 199.835 ] concat
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[
(Backwards propagation of error)
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[ 0.910058 0.414482 -0.414482 0.910058 298.451 761.096 ] concat
%I
[
(mean field theory)
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[ 0.921887 -0.38746 0.38746 0.921887 229.401 778.83 ] concat
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[
(sigma-pi units)
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[ 0.139468 -0.990227 0.990227 0.139468 44.6492 197.721 ] concat
%I
[
(course coding)
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[ 0.990621 0.136637 -0.136637 0.990621 99.9927 773.606 ] concat
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[
(sparse representations)
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[ 0.64204 -0.766671 0.766671 0.64204 37.8367 795.852 ] concat
%I
[
(relearning)
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Begin %I Text
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[ 0.803947 -0.594701 0.594701 0.803947 520.603 713.68 ] concat
%I
[
(multitask learning)
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Begin %I Text
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[ 0.988194 0.153208 -0.153208 0.988194 72.6487 689.571 ] concat
%I
[
(constructuve interference)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.907674 -0.419677 0.419677 0.907674 530.295 796.115 ] concat
%I
[
(real-world datasets)
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Begin %I Text
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[ 0.358851 -0.933395 0.933395 0.358851 45.035 394.372 ] concat
%I
[
(standard benchmarks)
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Begin %I Pict
%I b u
%I cfg u
%I cbg u
%I f u
%I p u
%I t u

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%I t
[ 2 0 0 2 50 740 ] concat
%I
[
(Neural Networks and Machine Learning)
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Begin %I Text
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%I t
[ 2 0 0 2 208 698 ] concat
%I
[
(ECE 547 / CS 591)
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Begin %I Text
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%I t
[ 1.2 0 0 1.2 202.6 625.1 ] concat
%I
[
(Fall 1998, Tue/Thur, 12:30-1:45)
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Begin %I Text
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%I t
[ 1.2 0 0 1.2 158.4 42.1 ] concat
%I
[
(http://www.cs.unm.edu/~bap/teach/F98/ECE547/)
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Begin %I Text
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%I f -*-times-bold-r-normal-*-14-*-*-*-*-*-*-*
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%I t
[ 1.2 0 0 1.2 206 644.1 ] concat
%I
[
(Professor Barak Pearlmutter)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-14-*-*-*-*-*-*-*
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%I t
[ 1.2 0 0 1.2 127.2 148.5 ] concat
%I
[
(You may be kidnapped and find yourself working)
(at a hidden data mine on the shady side of Silicon Valley.)
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Begin %I Text
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%I t
[ 1.44 0 0 1.44 282.72 171.42 ] concat
%I
[
(Beware!)
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Begin %I Text
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%I t
[ 1.2 0 0 1.2 182.1 306.9 ] concat
%I
[
(style: hands-on, experimental)
(goal: intuition and understanding)
(difficulty: commensurate with ability)
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Begin %I Text
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%I t
[ 1.2 0 0 1.2 91.6 398.3 ] concat
%I
[
(Those intrepid undergraduates and grad students who take this)
(cross-listed class will, by the end of the term, be qualified to apply)
(neural network techniques to a plethora of real-world scientific and)
(industrial problems.)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-14-*-*-*-*-*-*-*
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%I t
[ 1.2 0 0 1.2 134.1 531.3 ] concat
%I
[
(Somewhere between the soft underbelly of theoretical)
(neurobiology, the shoals of statistics, the ancient ruins of)
(cybernetics, and the firm bedrocks of signal processing and)
(control theory lies the newfound territory of neural networks)
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End

End %I eop

Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.0412316 0.99915 -0.99915 0.0412316 16.5966 596.725 ] concat
%I
[
(minimum mutual information)
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Begin %I Text
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0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.0567267 -0.99839 0.99839 0.0567267 575.53 256.586 ] concat
%I
[
(the method of temporal differences)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.633238 0.773957 -0.773957 0.633238 26.6921 466.996 ] concat
%I
[
(supervised learning)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.998998 -0.0447558 0.0447558 0.998998 106.234 438.435 ] concat
%I
[
(unsupervised learning)
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Begin %I Text
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%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
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%I t
[ 0.957098 0.289764 -0.289764 0.957098 27.2434 431.602 ] concat
%I
[
(reinforcement learning)
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Begin %I Text
%I cfg DkGray
0.503548 0.503548 0.503548 SetCFg
%I f -*-times-medium-r-normal-*-12-*-*-*-*-*-*-*
Times-Roman 12 SetF
%I t
[ 0.713336 0.700822 -0.700822 0.713336 194.377 546.521 ] concat
%I
[
(world champion)
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End

End %I eop

showpage

%%Trailer

end