Syllabus des cours - Université Lyon 2

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Deep$learning$
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Text$mining$
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Model-based$learning$
Pré-requis$:$ notions& de& probabilités& et& statistiques&:& variable& aléatoire,& loi& de& probabilité,&
indépendance,&estimation,&vraisemblance,&espérance&conditionnelle.(
Mots-clefs(5(+#$''2A2+$/2,3<(+#&'/*-234<(=,)6#*'()*(=7#$34*'<($#4,-2/?=*(\.<('7#*+/2,3()*(=,)6#*'(
Intervenants(5(C&#2*3(C$+>&*'(8E32:*-'2/7(F",3(0;(
Volume$horaire(5(0H?(
Objectifs(
M9-6'(&3(-$99*#(4737-$#()*'(9-23+29$#*'(=7/?,)*'()*(+#$''2A2+$/2,3(*/()*(+#&'/*-234<(#[,%Z*+/2A()*(
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Connaissances$à$acquérir(
F*(=,)6#*()*(=7#$34*<('*'(?"9,/?6'*'(*/('*'(+,3'7>&*3+*'(*3(/*-=*'()*(-64#*()*(+#$''2A2+$/2,3J((
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!$:,2-(-7',&)-*(&3(9-,%#6=*()*(+#$''2A2+$/2,3('&9*-:2'7*(,&(3,3('&9*-:2'7*($:*+()*'($99-,+?*'(
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Modalités$pédagogiques(
F*'('7$3+*'($#/*-3*-,3/(*3/-*(+,&-'(*/(=2'*(*3(9-$/2>&*(',&'(OJ(F*'(+,&-'('*-,3/(g(#$(A,2'(',&'(#$(
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Advanced$supervised$learning$
Pré-requis$:$notions&en&algèbre&linéaire,&en&optimisation,&en&modélisation&statistique(
Mots-clefs( 5( $99-*3/2''$4*( '&9*-:2'7<( -74-*''2,3<( +#$''*=*3/<( =,)6#*'( 973$#2'7'( */(
9$-+2=,32*&@<(=7/?,)*'(*3'*=%#2'/*'J(
Intervenants(5(C&#2*3(M?BI23*(8E32:*-'2/7(F",3(0;(
Volume$horaire(5(LT?(
Objectifs(
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Connaissances$à$acquérir$(

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Modalités$pédagogiques(
L’enseignement&comporte&une&partie&cours&magistral&de&21h&et&une&partie&mise&en&pratique/étude&de&
cas&de&9h.&Les&supports&de&cours&sont&mis&à&disposition&et&projetés&lors&des&séances.&Des&exercices&
devront& être& résolus& en& dehors& des& séances.& La& partie& mise& en& pratique& concerne& l’utilisation& du&
logiciel&R&pour&l’illustration&de&certains&concepts&vus&en&cours&et/ou&l’implémentation&d’algorithmes&
et/ou&l’étude&d’un&cas&pratique.&L’évaluation&comportera&un&examen&individuel&sur&table&et&un&dossier&
collectif&à&rendre&et&à&soutenir.&(
Manifold$learning$
Prérequis(5(_,/2,3'()[M#46%-*(=$/-2+2*##*(*/(,9/2=2'$/2,3((
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Objectifs(
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