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Thematic program on statistical inference, learning and models for Big Data

This is a 5-month multi-faceted program in statistical methodology for Big Data organized by the Fields Institute for Research in Mathematical Sciences, University of Toronto CA. Emphasis on both applied and theoretical aspects.
When 12 January 2015 11:55 AM to
14 June 2015 11:55 AM
Where Toronto CA
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Conferences and Workshops


Training

Graduate Course 1:.Large Scale Machine Learning 
Monday, 11 a.m. -2 p.m, January 5 to March 31 ( no classes Feb 16-20), Stewart Library, Fields Institute

Instructor: Russ Salakhutdinov, Departments of Computer Science and Statistical Sciences, University of Toronto

Description: Statistical machine learning is a very dynamic field that lies at the intersection of statistics and computational sciences. The goal of statistical machine learning is to develop algorithms that can "learn" from data using statistical and computational methods. Over the last decade, driven by rapid advances in numerous fields, such as computational biology, neuroscience, data mining, signal processing, and finance, applications that involve large amounts of high-dimensional data are not that uncommon.
The goal of this course is to introduce core concepts of large-scale machine learning and discuss scalable techniques for analyzing large amounts of data. Both theoretical and practical aspects will be discussed.

Graduate Course 2: Topics in Inference for Big Data 
Friday, 1 p.m. -4 p.m, January 5 to March 31 ( no classes Feb 16-20), Stewart Library, Fields Institute
Instructors: Nancy Reid, Department of Statistical Sciences, University of Toronto; Mu Zhu, Department of Statistics and Actuarial Science, University of Waterloo

Description: This course will introduce students to the topics under discussion during the thematic program on Statistical Inference in Big Data, with a mix of background lectures and guest lectures. The goal is to prepare students, postdoctoral fellows, and other interested participants to benefit from upcoming workshops in the thematic program, and to provide a venue for further discussion of keynote presentations after the workshops.

These courses will be streamed using FieldsLive, and students are welcome to attend online. Students interested in obtaining credit for these courses need to arrange with their home department to have them approved as reading or research courses. We will make available the timetable and requirements for the course at the first lecture in January, 2015.

Postdoctoral fellowships

A limited number of postdoctoral fellowships are available; please see the Fields web page for the advertisement. Applications were due by June 1, 2014 but late applications will also be accepted until the positions are filled. There are opportunities for extended visits of senior (all but degree) graduate students. Please apply through the Application for Participant Support link. 

Distinguished Lectures

Distinguished Lecture Series, and
2015 Distinguished Lecture Series in StatisticsTerry Speed (University of California, Berkeley)

Allied Activity

July 21 – August 15, 2014
Summer School: Statistical Learning in Big Data
Instructors: Hugh Chipman, Acadia; Sunny Wang, St. Francis Xavier
held at AARMS 

December 10 & 11, 2014
Distinguished Lecture Series in Statistical Science
Bin Yu, University of California, Berkeley 
Room 230, Fields Institute

February/March, 2015
General Scientific Activity: Big Data in Commercial and Retail Banking (1 day)
with Mark Reesor, (Western); Matt Davison, (Western); Adam Metzler, (Wilfrid Laurier )
held at Fields Institutue

April 20 - 24, 2015 
CANSSI workshop 

 

April 20 – 24, 2015 
Workshop Statistical Theory for Large-Scale Data
with Richard Lockhart (Chair), Nicolai Meinshausen
held at PIMS, University of British Columbia 

May 4 – 8, 2015 
Workshop and Short Course on Statistical and computational challenges in networks, web mining and cybersecurity: 
with Hugh Chipman (Chair), François Théberge (U Ottawa)
held at CRM, Montreal 

May 11–15, 2015
Workshop on Challenges in Environmental Science
with Richard Lockhart (Chair), James Zidek (UBC) 
held at PIMS, University of British Columbia

August, 2015
Workshop on Deep Learning
Organizing Committee: Yoshua Bengio, Chair 
held at CRM, Montreal

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Workshop Overviews

Preliminary descriptions of the workshops and conferences, from the program proposal.

Jan 12-13: Introductory Lectures and Overview
Jan 14: Inference
Jan 15: Environmental Science
Jan 16: Optimization
Jan 19: Visualization
Jan 20: Social Policy
Jan 21: Health Policy
Jan 22: Deep Learning
Jan 23: Networks and Machine Learning

More information about this event…