Archive for the ‘talks’ Category

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Machine Learning Research Tutorials

March 8, 2020

Machine learning has become one of the hottest areas in computer science and technology. Both industry and academia have gone gaga. Big tech companies send 100’s each to the top research conferences and the conference numbers are increasing in size so they are now beyond capacity. But, how do you learn about machine learning in the first place? Assuming you have a strong STEM undergraduate degree and are research savvy, this page points to some appropriate resources for research. These are intended for starting PhD students.

If you are more interested in learning the basics as a potential user, then you will need to find different resources such as the blogs up on https://medium.com/search?q=machinelearning&ref=opensearch or at the MOOCS such as Coursera.

University Classes

Places like Stanford and CMU have very good advanced masters-level classes ideal for starting PhD students. Slides and oftentimes lectures are online for the public. e.g., deep networks for NLP http://web.stanford.edu/class/cs224n/

See also Lex Fridman’s seminars up at https://deeplearning.mit.edu/ . Very good overview of capabilities and directions for a general overview.

Good Venues

Excellent tutorials are available recently at the major conferences, oftentimes with vidoes and/or slides on the website, although sometimes you have to hunt through the author’s webpages. The top conferences include AI&Stats, IJCAI, ICML, ACL … be warned, some tutorials are a bit specialised or advanced.

Machine Learning Summer School (MLSS)

This series is managed by venerable machine learning researchers and only has a few per year internationally. Their list of venues is at http://mlss.cc/ . You have to go to each and navigate disparate and sometimes wacky layouts to locate slides and/or videos.

AutoML

The Freiburg-Hannover group has a great sequence of tutorials on AutoML and learning to learn:

VideoLectures.net

An initiative of the Jozef Stefan Institute, Ljubljana, records many great tutorials, but coverage not as good recently. Go seaching for your favorite subjects:

Others

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Machine Learning tutorial at ACSW 2020

February 5, 2020

Australasian Computer Science Week is a collection of computer science events for Australian and New Zealand CS researchers. I’m giving a tutorial as part of their HDR/ECR programme on Machine Learning. Machine Learning has gone crazy in the last few years, growing exponentially and with ever vanishing publishing cycles, entering its own singularity I believe. So my slides are quite general and covering big issues rather than lots of detail. Its a longer talk so I’ll be skipping a few slides. I left some of the math in for interested readers that I wont cover much in the talk. As always, way too many perspectives and variations to pick from but I am focusing on probabilistic interpretations.

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Interview with Monash Tech Talks team

December 16, 2019

I did another interview, MCd by our Dean John Whittle and Dr. Catherine Lopes, again on AI and machine learning.  This one was professionally organised with a green screen and in an official interview studio.  I had to find a plain, non-green shirt, no stripes or patterns!

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Prof. John Whittle, Dr. Catherine Lopes and Wray at an interview run by Monash Tech Talks at the Redback Conferencing facilities, 9th Dec 2019

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Perspectives on Career Pathways

November 9, 2019

Wray and Adel Toosi at ECR Workshop

Wray with Adel Toosi at the ECR workshop, receiving some fabulous Aboriginal artwork.

I gave a talk to the Early Career Researchers in our faculty.  They had held a workhop on career pathways.  I showed them the crazy ride I’ve had (a so-called career path) and talked about things like “Know Your Strengths”, “Long Term Planning”, “Preparing for Industry” and “Preparing for More Research”.  My slides (slightly updated) are available here.  Adel put the original ones up on the Monash share drive.

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Southeast Asia Machine Learning School

July 9, 2019

Very fortunate to be asked to give a lecture on “Foundations of Supervised Learning” at SEA-MLS in Jakarta on 8th July.  The school was co-organised by Google, so opening talk by Google and a member of the Indonesian government.  A big crowd too!  Everyones slides are up on the schedule page.

Never been to Jakarta so an exciting opportunity meet some colleagues, some students, in a lovely environment. Monash has a school at Malaysia so a few Malaysia Monash folks turned up too.  Here we are after my lecture.

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Monash Malaysia and Melbourne students at SEA-MLS.

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Invited talk at ACML in Beijing

October 11, 2018

I’ve given an invited talk at ACML in Beijing November:  see Invited Speakers at the ACML website.

Wray's talk at ACML 2018

I talked about the state of Machine Learning, contrasting the old with the new, and discuss where we may head next.  Moreover, I gave some warnings about some problems we are currently facing.  PDF slides for the talk are here.  Abstract is given below.  Prof. Jun Zhu (Tsinghua U.) has had some similar ideas so we conferred afterwards.

Several of us from Monash went:  in the picture are Ye Zhu, Wray Buntine, Lan Du, Yuan Jin and He Zhao.Monash (past and present) at ACML 2018

Something Old, Something New, Something Borrowed, Something Blue

Something Old: In this talk I will first describe some of our recent work with hierarchical probabilistic models that are not deep neural networks. Nevertheless, these are currently among the state of the art in classification and in topic modelling: k-dependence Bayesian networks and hierarchical topic models, respectively, and both are deep models in a different sense. These represent some of the leading edge machine learning technology prior to the advent of deep neural networks. Something New: On deep neural networks, I will describe as a point of comparison some of the state of the art applications I am familiar with: multi-task learning, document classification, and learning to learn. These build on the RNNs widely used in semi-structured learning. The old and the new are remarkably different. So what are the new capabilities deep neural networks have yielded? Do we even need the old technology? What can we do next? Something Borrowed: to complete the story, I’ll introduce some efforts to combine the two approaches, borrowing from earlier work in statistics.

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ECML-PKDD talk on Bayesian network classifiers

September 14, 2018

On the 14th September 2018 I presented the following paper at ECML-PKDD in Dublin.  The slides for the talk are here.

We figured out how to do good smoothing of Bayesian network classifiers.  The same technique works for decision trees, and in fact beats all known algorithms for smoothing/pruning!

Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes”, by François Petitjean, Wray Buntine, Geoffrey I. Webb and Nayyar Zaidi, in Machine Learning, 18th May 2018, DOI 10.1007/s10994-018-5718-0.  Available online at Springer Link.  Presented at ECML-PKDD 2018 in Dublin in September, 2018.