“The courses were all super interesting and linked well with each other and the program extras were really valuable for helping to connect with the other attendees and organisers.”

Michael Groom, The University of Sydney

Timetable

AMSI Winter School 2021 will focus on the theme ‘statistical data science’ through the delivery of a series of short courses throughout the two-week timetable. In addition, a number of program extras are organised and included into the program to maximise the experience.

Attendance is expected at all scheduled events listed below. To find out more about the program being offered, please visit the course guide and lecturers page.

Please note: Timetable may be subject to change

Key

An Introduction to Bayesian Statistics
Professor Gael Martin		 Dimension Reduction: A plane and simple primer on linear and nonlinear algorithms with applications
Associate Professor Anastasios Panagiotelis
Neural Networks and Related Models
Dr Susan Wei		 Post-Processing of MCMC
Dr Leah South
Approximate Bayesian Computation: The Likelihood is dead, Long Live Simulation!
Associate Professor David Frazier		 Neural Networks and Related Models
Dr Robert Salomone
Subsampling MCMC – An approach to speed up MCMC by data subsampling
Dr Matias Quiroz
TimeTable (Week 1)
WA SA
 QLD/NSW/ACT/VIC/TAS Monday | 12 July Tuesday | 13 July Wednesday | 14 July Thursday | 15 July Friday | 16 July
8.00am – 8.30am 9.30am – 10.00am 10.00am – 10.30am Welcome Dimension Reduction – Lecture 2
 Neural Networks and Related Models -Lecture 2
 Post-Processing of MCMC – Lecture 1 Participant Talks

(starting at 10.15am AEST)
8.30am – 9.00am 10.00am – 10.30am 10.30am – 11.00am
9.00am – 9.30am 10.30am – 11.00am 11.00am – 11.30am An Introduction to Bayesian Statistics – Lecture 1

(Including short break)
9.30am – 10.00am 11.00am – 11.30am 11.30am – 12.00am Break Break Break Break
10.00am – 10.30am 11.30am – 12.00pm 12.00pm – 12.30pm Dimension Reduction – Tutorial
 Neural Networks and Related Models – Tutorial Dimension Reduction – Lecture 4
 Participant Talks
10.30am – 11.00am 12.00pm – 12.30pm 12.30pm – 1.00pm
11.00am – 11.30am 12.30pm – 1.00pm 1.00pm – 1.30pm Lunch Break Lunch Break Lunch Break Lunch Break
11.30am – 12.00pm 1.00pm – 1.30pm 1.30pm – 2.00pm Lunch Break

(Mindfulness Session 1.30-2pm AEST)
12.00pm – 12.30pm 1.30pm – 2.00pm 2.00pm – 2.30pm Dimension Reduction – Lecture 1 An Introduction to Bayesian Statistics – Lecture 2
(Including short break)		 Dimension Reduction – Lecture 3
 Post-Processing of MCMC – Lecture 2
12.30pm – 1.00pm 2.00pm – 2.30pm 2.30pm – 3.00pm Neural Networks and Related Models -Lecture 4
1.00pm – 1.30pm 2.30pm – 3.00pm 3.00pm – 3.30pm
1.30pm – 2.00pm 3.00pm – 3.30pm 3.30pm – 4.00pm Break Break Break
2.00pm – 2.30pm 3.30pm – 4.00pm 4.00pm – 4.30pm Neural Networks and Related Models –Lecture 1 Break Neural Networks and Related Models -Lecture 3
 Break Post-Processing of MCMC – Tutorial
2.30pm – 3.00pm 4.00pm – 4.30pm 4.30pm – 5.00pm An Introduction to Bayesian Statistics – Tutorial Dimension Reduction – Tutorial
3.00pm – 3.30pm 4.30pm – 5.00pm 5.00pm – 5.30pm
Evening Evening Evening Celebration of Mathematical Sciences event
(5.45pm – 6.45pm AEST)		 Friday Night Social at local event hubs
(5.30pm – 7pm AEST)
TimeTable (Week 2)
WA SA
 QLD/NSW/ACT/VIC/TAS Monday | 19 July Tuesday | 20 July Wednesday | 21 July Thursday | 22 July Friday | 23 July
8.00am – 8.30am 9.30am – 10.00am 10.00am – 10.30am Approximate Bayesian Computation – Lecture 1 Approximate Bayesian Computation –  Lecture 2
 Subsampling MCMC –Lecture 2
 Subsampling MCMC – Tutorial
 Approximate Bayesian Computation – Lecture 4
8.30am – 9.00am 10.00am – 10.30am 10.30am – 11.00am
9.00am – 9.30am 10.30am – 11.00am 11.00am – 11.30am Break
9.30am – 10.00am 11.00am – 11.30am 11.30am – 12.00am Break Break Break Subsampling MCMC –Lecture 3
 Break
10.00am – 10.30am 11.30am – 12.00pm 12.00pm – 12.30pm Neural Networks and Related Models -Lecture 1 Approximate Bayesian Computation – Tutorial
 Neural Networks and Related Models – Tutorial Approximate Bayesian Computation – Tutorial
10.30am – 11.00am 12.00pm – 12.30pm 12.30pm – 1.00pm
11.00am – 11.30am 12.30pm – 1.00pm 1.00pm – 1.30pm Lunch Break Lunch Break

(Mindfulness Session 1pm – 1.30pm AEST)

 Lunch Break Lunch Break
11.30am – 12.00pm 1.00pm – 1.30pm 1.30pm – 2.00pm Lunch Break
12.00pm – 12.30pm 1.30pm – 2.00pm 2.00pm – 2.30pm Neural Networks and Related Models -Lecture 2
 Approximate Bayesian Computation Lecture 3
 Neural Networks and Related Models -Lecture 4
 Subsampling MCMC –Lecture 4
12.30pm – 1.00pm 2.00pm – 2.30pm 2.30pm – 3.00pm Participant Talks
1.00pm – 1.30pm 2.30pm – 3.00pm 3.00pm – 3.30pm
1.30pm – 2.00pm 3.00pm – 3.30pm 3.30pm – 4.00pm Break Break Break Break
2.00pm – 2.30pm 3.30pm – 4.00pm 4.00pm – 4.30pm Break Subsampling MCMC –Lecture 1 Neural Networks and Related Models -Lecture 3
 Neural Networks and Related Models – Tutorial Subsampling MCMC – Tutorial
2.30pm – 3.00pm 4.00pm – 4.30pm 4.30pm – 5.00pm Participant Talks
3.00pm – 3.30pm 4.30pm – 5.00pm 5.00pm – 5.30pm
Evening Evening Evening Tuesday Trivia Night (5.30pm – 7pm AEST)
 Public Lecture

(6.30pm – 7.30pm AEST)

Program Extras

Winter School has a range of extra activities so you can network and get the most out of the program.

VIEW PROGRAM EXTRAS