Dr. Dmitry Efimov is a world expert in data mining. He won prizes for modelling and solving problems for industrial companies. He will give a series of two lectures in Tunis on Tuesday April 2nd (at l'ENIT) and Wednesday April 3rd (at l'EPT).
Dans le monde avancé, 80% de la recherche scientifique se fait en dehors de l'université. Le monde de l'industrie et de l'entreprise fait donc appel de plus en plus à une expertise mathématique décentralisée. Des interfaces mathématiques (sous formes de workshops et surtout de sites web) se sont mises en place pour faire participer les mathématiciens du monde entier dans la résolution de problèmes mathematiques (travail de groupe, participation online, etc). Le MIMS invite un lauréat dans le domaine lequel offre de parler de ces nouvelles plateformes et de leur fonctionnement, ainsi que de faire le tour d'horizon des outils mathématiques les plus utiles dans la résolution de tels problèmes.
Organizing Commitee:
MIMS avec l'aide du Laboratoire d'ingenierie Mathematiques (EPT, Hasna Riahi)
Sponsors:
MIMS, EPT
Speakers: Dmitry Efimov Moscow State University and American University of Sharjah
Tuesday April 2nd, ENIT, Salle Chaire Unesco, 14h
Lecture on Advanced Machine Learning: techniques and methods.
The following topics will be covered: data examination and cleaning, feature selection and engineering, Support Vector Machines, Neural Networks, Gradient Boosting Decision Trees, Random Forests.
Wednesday April 3rd, Ecole Polytechnique de Tunis
Amphi Ibn Khaldoun, 14h30
Introduction to data mining (lecture for undergradute students).
Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. In my talk I will give the introduction to the data mining algorithms and show how to transform the information from Facebook, LinkedIn and Gmail to the mathematical model to predict the strength of relationships in social network graphs. The reported results were obtained online during one of the private data mining competition on the Kaggle platform in 2012.
Speakers: Dmitry Efimov Moscow State University and American University of Sharjah
Tuesday April 2nd, ENIT, Salle Chaire Unesco, 14h
Lecture on Advanced Machine Learning: techniques and methods.
The following topics will be covered: data examination and cleaning, feature selection and engineering, Support Vector Machines, Neural Networks, Gradient Boosting Decision Trees, Random Forests.
Wednesday April 3rd, Ecole Polytechnique de Tunis
Amphi Ibn Khaldoun, 14h30
Introduction to data mining (lecture for undergradute students).
Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. In my talk I will give the introduction to the data mining algorithms and show how to transform the information from Facebook, LinkedIn and Gmail to the mathematical model to predict the strength of relationships in social network graphs. The reported results were obtained online during one of the private data mining competition on the Kaggle platform in 2012.