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Other Seminars

Other Seminars

Genetic Methods for Biology and Epidemiology

October 1, 2021

Speakers:

Benjamin Neale

Dr.

Bayesian Modelling and Analysis of Challenging Data: Identifying the Intrinsic Dimension of High-Dimensional Data

September 21, 2021

Speakers:

Kerrie Mengersen

One of the challenges of high-dimensional data is identifying the true information contained therein.

Bayesian Modelling and Analysis of Challenging Data: Making New Sources of Data Trustworthy

September 20, 2021

Speakers:

Kerrie Mengersen

Evidence-based decisions depend critically on trustworthy data.

Data Science & Machine Learning at Vanguard

May 17, 2021

Speakers:

Eduardo Fontes

TBC

MScAC Program Info Sessions

May 6, 2021 to August 26, 2021

MScAC is hosting a series of info sessions designed to talk more about the program (from what makes this program unique to how to apply) and about

Picture of a faculty member Jeffrey Rosenthal

Writers on Writing

February 26, 2021

Speakers:

Jeffrey Rosenthal

From senior scholars to students, Writers on Writing shines a spotlight on Arts & Science community members who write about a dizzying array of

Toronto Data Workshop on Reproducibility

February 25, 2021 to February 26, 2021

Speakers:

Rohan Alexander, University of Toronto

The Faculty of Information and the Department of Statistical Sciences at the University of Toronto are excited to host a two-day conference bringin

Evaluating Machine Learning Claims

December 21, 2020

Speakers:

Michael M. Hoffman, University of Toronto

Machine learning has already changed many areas of our society, and there is the promise of it revolutionizing others.

Statistics and Genetics Offer a Window into Autism

December 4, 2020

Speakers:

Kathryn Roeder, Carnegie Mellon University

Recently the largest exome sequencing study to date of autism spectrum disorder (ASD) implicated 102 genes in risk.

Towards the finite-sample variance bounds for unbiased estimators

June 5, 2019

Speakers:

Ioannis Kosmidis (University of Warwick)

The inverse of the Fisher information matrix in a likelihood problem is i) the variance-covariance matrix of the asymptotic distributio

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