IVADO

Opportunités et limites de l'apprentissage automatique (machine-learning) pour les sciences appliquées (en anglais)
2016-02-26 14:00 - 15:00 (America/New_York) | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Présentation de Mohammad Attarian Shandiz – Université McGill, Canada
Modern methods in machine learning have provided many opportunities for solving complex problems in applied science. Hence, for a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. In this talk, the most significant fields of research in machine learning and data mining are introduced based on the survey in the database of scientific journals. Subsequently, various applications of machine learning for some challenging problems in medicine, finance and engineering are discussed. Lastly, the results of optimized classifiers for a classification problem in the field of lithium-ion batteries are presented. Ensemble methods including random forests and extremely randomized trees provided the highest accuracy of prediction among other methods for the classification based on the Monte Carlo cross validation tests.
Séminaire du GERAD
26 FÉV. 2016 14H00 – 15H00
Salle 4488
Pavillon André-Aisenstadt
Campus de l’Université de Montréal
2920, chemin de la Tour Montréal QC H3T 1J4 Canada

Optimiser les infrastructures du Cloud de Google (en anglais)
2016-02-19 10:45 - 12:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Conférence de Brian Eck – Google, États-Unis
Google’s Cloud infrastructure consists of the servers, network, datacenters and software that run all of Google’s services like Search, Gmail and Youtube as well as it’s fast growing external business, Google Cloud Platform. We’ll discuss three case studies in optimizing this infrastructure, covering challenges not only in solving the analytical problems but also in solving the business problems to reach real implementation and impact. Case studies will include some subset of:

Optimizing build frequency for adding capacity to the network,
Analyzing scenarios of metro network topology to inform strategy, including cost implications of adjusting forecasts,
Building ‘forecast tolerant’ networks that buffer for fault-tolerance and forecast variation,
Low-cost methods of physical grooming on a city-level fiber infrastructure,
Reducing stranded compute resources through simulation,
Increasing resource utilization through statistical oversubscription.

Séminaire du GERAD
Le 19 Février 2016 de 10H45 – 12H00
Salle 4488
Pavillon André-Aisenstadt
Campus de l’Université de Montréal
2920, chemin de la Tour Montréal QC H3T 1J4 Canada

Big data & mixed-integer (nonlinear) programming
2016-01-22 16:00 - 17:00 | UQÀM, Pavillon Président-Kennedy, Salle PK 5115, 201, avenue du Président-Kennedy, Montreal, québec, Canada
Andrea Lodi – Professeur titulaire, Département de mathématiques et de génie industriel, Polytechnique Montréal, Canada
Conférence en anglais
In this talk I review a couple of applications on Big Data that I personally like and I try to explain my point of view as a Mathematical Optimizer — especially concerned with discrete (integer) decisions — on the subject. I advocate a tight integration of Data Mining, Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. Those challenges are the core of the mission of the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” that I hold.

GERAD: Reverse Stackelberg games: Theory and applications in traffic control
2016-01-18 10:00 - 12:00 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada

Yoshua Bengio: Intelligence Artificielle Et Apprentissage Profond
2015-09-10 | Université de Montréal, Pavillon André-Aisenstadt, Pavillon André-Aisenstadt, Montréal, QC, H3T 1J4, Canada
Conférence de Yoshua Bengio, professeur en Intelligence artificielle, titulaire au Département d’informatique et de recherche opérationnelle de l’Université de Montréal.
