Return-Path: Delivered-To: cadet@irif.fr Received: from mailhub.math.univ-paris-diderot.fr ([81.194.30.253]) by mailhost.irif.fr (Dovecot) with LMTP id 9ZDVCA65n2GB9AAAP9ZUWg for ; Thu, 25 Nov 2021 17:25:50 +0100 Received: from mailhub.math.univ-paris-diderot.fr (localhost [127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTP id BBE0EFDB7F; Thu, 25 Nov 2021 17:25:50 +0100 (CET) X-Virus-Scanned: amavisd-new at math.univ-paris-diderot.fr X-Spam-Flag: NO X-Spam-Score: -1.628 X-Spam-Level: X-Spam-Status: No, score=-1.628 tagged_above=-10000 required=5 tests=[BAYES_00=-1.9, HEADER_FROM_DIFFERENT_DOMAINS=0.249, HTML_MESSAGE=0.001, MAILING_LIST_MULTI=-1, MISSING_HEADERS=1.021, URIBL_BLOCKED=0.001] autolearn=no autolearn_force=no Received: from mailhub.math.univ-paris-diderot.fr ([127.0.0.1]) by mailhub.math.univ-paris-diderot.fr (mailhub.math.univ-paris-diderot.fr [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id zJW1D08Tx-xt; Thu, 25 Nov 2021 17:25:48 +0100 (CET) Received: from potemkin.univ-paris7.fr (potemkin.univ-paris7.fr [194.254.61.141]) by mailhub.math.univ-paris-diderot.fr (Postfix) with ESMTPS id A7810FDB77; Thu, 25 Nov 2021 17:25:48 +0100 (CET) Received: from mars.math-info.univ-paris5.fr (helios2.math-info.univ-paris5.fr [193.48.200.16]) by potemkin.univ-paris7.fr (8.14.4/8.14.4/relay2/82085) with ESMTP id 1APGPmcI002173 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits=256 verify=OK); Thu, 25 Nov 2021 17:25:48 +0100 Received: from mars.math-info.univ-paris5.fr (localhost [127.0.0.1]) by mars.math-info.univ-paris5.fr (8.14.4/jtpda-5.4) with ESMTP id 1APGPlHO028190 ; Thu, 25 Nov 2021 17:25:47 +0100 Received: (from sympa@localhost) by mars.math-info.univ-paris5.fr (8.14.4/8.14.3/Submit) id 1APGPlFB028180; Thu, 25 Nov 2021 17:25:47 +0100 X-Authentication-Warning: mars.math-info.univ-paris5.fr: sympa set sender to diip-perimeter-owner@math-info.univ-paris5.fr using -f X-Sympa-To: diip-perimeter@math-info.univ-paris5.fr Received: from smtp-out02.parisdescartes.fr (mx1.parisdescartes.fr [193.51.86.61]) by mars.math-info.univ-paris5.fr (8.14.4/jtpda-5.4) with ESMTP id 1APFcASv026651 for ; Thu, 25 Nov 2021 16:38:10 +0100 Received: from localhost (saroumane.univ-paris5.fr [192.168.253.9]) by mx1.parisdescartes.fr (Postfix) with ESMTP id 70860242530; Thu, 25 Nov 2021 16:38:10 +0100 (CET) X-Virus-Scanned: amavisd-new at univ-paris5.fr Received: from mx1.parisdescartes.fr ([192.168.253.61]) by localhost (fourmilier.univ-paris5.fr [192.168.253.9]) (amavisd-new, port 10024) with ESMTP id zijxjOkGyebp; Thu, 25 Nov 2021 16:38:07 +0100 (CET) Received: from goustan.fougeres.campus (goustan.fougeres.campus [172.22.6.50]) by smtp.parisdescartes.fr (Postfix) with ESMTPS id 8D25324255A; Thu, 25 Nov 2021 16:38:07 +0100 (CET) Received: from MORDRED.fougeres.campus (172.17.143.89) by goustan.fougeres.campus (172.22.6.50) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256) id 15.1.2308.20; Thu, 25 Nov 2021 16:38:07 +0100 Received: from MORDRED.fougeres.campus ([fe80::98b5:8278:2156:252b]) by mordred.fougeres.campus ([fe80::98b5:8278:2156:252b%10]) with mapi id 15.01.2308.020; Thu, 25 Nov 2021 16:38:07 +0100 From: =?iso-8859-1?Q?Ana=EFs_De_Muret_De_Labouret?= CC: "diip@math-info.univ-paris5.fr" Thread-Topic: diiP Seminar, Dec15 4pm Thread-Index: AQHX4hHyMcmvN5Y9ekeqpSNIqNBb3Q== Date: Thu, 25 Nov 2021 15:38:07 +0000 Message-ID: Accept-Language: fr-FR, en-US Content-Language: fr-FR X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [172.17.143.79] Content-Type: multipart/alternative; boundary="_000_f6e553b4f73349e9a9be1c5f5eac42cfparisdescartesfr_" MIME-Version: 1.0 X-Validation-by: themis@mi.parisdescartes.fr Subject: [diip-perimeter] diiP Seminar, Dec15 4pm Reply-To: =?iso-8859-1?Q?Ana=EFs_De_Muret_De_Labouret?= X-Loop: diip-perimeter@math-info.univ-paris5.fr X-Sequence: 40 Errors-to: diip-perimeter-owner@math-info.univ-paris5.fr Precedence: list Precedence: bulk Sender: diip-perimeter-request@math-info.univ-paris5.fr X-no-archive: yes List-Id: List-Archive: List-Help: List-Owner: List-Post: List-Subscribe: List-Unsubscribe: X-Greylist: Sender succeeded STARTTLS authentication, not delayed by milter-greylist-4.2.7 (potemkin.univ-paris7.fr [194.254.61.141]); Thu, 25 Nov 2021 17:25:48 +0100 (CET) X-Miltered: at potemkin with ID 619FB90C.000 by Joe's j-chkmail (http : // j-chkmail dot ensmp dot fr)! X-j-chkmail-Enveloppe: 619FB90C.000 from helios2.math-info.univ-paris5.fr/helios2.math-info.univ-paris5.fr/null/mars.math-info.univ-paris5.fr/ X-j-chkmail-Score: MSGID : 619FB90C.000 on potemkin.univ-paris7.fr : j-chkmail score : . : R=. U=. O=. B=0.000 -> S=0.000 X-j-chkmail-Status: Ham --_000_f6e553b4f73349e9a9be1c5f5eac42cfparisdescartesfr_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable diiP Seminar Ensemble Learning: Theory and Techniques Who: Dr Foula Vagena (Universit=E9 de Paris, diiP) When: December 15, 4pm (Paris time) Where: online (zoom) https://u-paris.zoom.us/j/85039567470?pwd=3DWFVacGhhMW= JRdzdUNDNrck9ZbW5Pdz09 title: Ensemble Learning: Theory and Techniques + Hands-on workshop abstract: Ensemble learning is the process by which multiple models, such as classifi= ers or experts, are combined to solve a particular computational intelligen= ce problem. Ensemble learning is primarily used to improve the (classificat= ion, prediction, function approximation, etc.) performance of a model, or r= educe the likelihood of an unfortunate selection of a poor one. By strategi= cally combining multiple models one can produce a new predictive model with= reduced variance, bias and improved predictions. In this tutorial we will = explain the bias-variance tradeoff and describe how popular ensemble techni= ques (such as bagging, boosting, stacking etc) handle it. We will conclude = the tutorial with an illustrative prediction task using various ensemble mo= dels. The Hands-On Workshop will focus on examples of ensemble models. short bio: Zografoula Vagena is a research associate at the Data Intelligence Institut= e of Paris (diiP) and affiliated with the Universit=E9 de Paris. She has be= en a data science researcher and practitioner for over ten years. She has w= orked on different analytics problems including forecasting, image processi= ng, graph analytics, multidimensional data analysis, text processing, recom= mendation systems, sequential data analysis and optimization within various= fields such as transportation, healthcare, retail, finance/insurance and a= ccounting. She has also performed research in the intersection of data mana= gement and analytics, and was a primary contributor of the MCDB/SimSQL syst= ems that blended data management with Bayesian statistics. She holds a PhD = in data management from the University of California, Riverside. Kind regards, Ana=EFs Ana=EFs de Muret Manager de projets, Cit=E9 du Genre et Data Intelligence Institute of Paris Universit=E9 de Paris 5, rue Thomas Mann B=E2timent C des Grands Moulins - Bureau 879C 75013 Paris 01 57 27 52 36 --_000_f6e553b4f73349e9a9be1c5f5eac42cfparisdescartesfr_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable

diiP Seminar 

Ensemble Learning: Theory and Techniques

Who: Dr Foula Va= gena (Universit=E9 de Paris, diiP)
When: December 1= 5, 4pm (Paris time)
Where: online (z= oom) https://u-paris.zoom.us/j/85039567470?pwd=3DWFVacGhhMWJRdzdUND= Nrck9ZbW5Pdz09


title:
Ensemble Learning: Theory and Techniques + Hands-on workshop

abstract:
Ensemble learning is the process by which multiple models, such as classifi= ers or experts, are combined to solve a particular computational intelligen= ce problem. Ensemble learning is primarily used to improve the (classificat= ion, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate s= election of a poor one. By strategically combining multiple models one can = produce a new predictive model with reduced variance, bias and improved pre= dictions. In this tutorial we will explain the bias-variance tradeoff and describe how popular ensemble techn= iques (such as bagging, boosting, stacking etc) handle it. We will conclude= the tutorial with an illustrative prediction task using various ensemble m= odels.

The Hands-On Workshop will focus on examples of ensemble models.

short bio:
Zografoula Vagena is a research associate at the Data Intelligence Institut= e of Paris (diiP) and affiliated with the Universit=E9 de Paris. She has be= en a data science researcher and practitioner for over ten years. She has w= orked on different analytics problems including forecasting, image processing, graph analytics, multidimensional= data analysis, text processing, recommendation systems, sequential data an= alysis and optimization within various fields such as transportation, healt= hcare, retail, finance/insurance and accounting. She has also performed research in the intersection of dat= a management and analytics, and was a primary contributor of the MCDB/SimSQ= L systems that blended data management with Bayesian statistics. She holds = a PhD in data management from the University of California, Riverside.


Kind regards,

Ana=EFs



Ana=EFs de Muret

Manager de projets, Cit= =E9 du Genre et Da= ta Intelligence Institute of Paris

Universit=E9 de Paris


5, rue Thomas Mann

B=E2timent C des Grands Moulins - Bureau= 879C

75013 Paris


01 57 27 52 36 

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