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.
Reza Haffari put together a team and we won a bid in the DARPA Learning with Less Labels (LwLL) programme. The picture below shows us all on 14th August discussing plans prior to flying off to Arlington, Virginia, to take part in the starting workshop. Our team comprises the 5 smiling academics below as well as Prof. Anton ven den Hengel of University of Adelaide. Reza, Dinh and I made the long haul to Arlington for 26-27th August 2019, where we met some really smart folks in the other teams!
The Monash DARPA LwLL team: Reza, Xiaojun, Dinh, Geoff and Wray
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, and mine are copied here.
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.
Monash Malaysia and Melbourne students at SEA-MLS.
Visited my old workplace, University of Helsinki, where I visited various faculty like Prof. Petri Myllymaki, Teemu Roos, Arto Klami and others. Across the bay is Aalto University with folks like Prof. Sami Kaski. The machine learning groups in broader Helsinki are very strong, with loads of researchers, quality students, and a very vigorous start-up and high-tech culture.
Gave a talk at Machine Learning Coffee Seminar on 17/12 (which serves porridge as well as coffee), and attended the AI Day on 12/12. The AI Day is organised by the recently convened Finnish Centre for Artifical Intelligence, and the speakers were senior researchers from Finland describing their broader vision and direction, so really interesting stuff and very educational. Lots of creativity in there, for instance combining simulation, machine learning, HCI and cognitive psychology.
Our Master of Data Science students explore a lot of things and discuss. I got a lot of requests to include the excellent material from DataCamp:
DataCamp – who support data science education for free
So we’ll see how it goes. Not sure how well I’ll get to integrate it, because this semester I’m working more on our introductory statistics class.
So most of this year I spent doing the Introduction to Data Science (introductory unit at Monash) and getting the Grad. Dip. of Data Science and the Master of Data Science up and running (some background here).
As a result, you can see the disastrous impact it has had on my Github activity, which is a measure of my coding productivity!
Wray’s activity on Github for 2015
On 11th-14th January 2016 I’ll be visiting the School of IT at Monash University Malaysia, which is located within the Bandar Sunway township in Malaysia just outside Kuala Lumpur city. My talk should be on the Monday (11th). The slides are here (available temporarily).
Title: Introduction to Data Science
This 2 hour seminar works through some of the emerging highlights of Data Science, reviewing major videos, blogs and articles that helped mold the field. This seminar looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. So the series is a broad introduction to working with data rather than a deep dive into the world of statistics. The seminar is aimed at those with an IT background who either want to start in Data Science or work with it, for instance in management or as a data engineer. Attendees should have a knowledge of information technology and computer science.
The talk will be extracted from our FIT5145 unit given in the Master of Data Science.
For my main job, I am Director of the Master of Data Science. This is a fast paced field that is just as much industry as academia, and a lot of the really exciting stuff is applications. To keep up you need to monitor the media. There are too many resources to name or list them all, or to attempt to do some kind of thorough tracking. I recommend students, however, to install a news aggregator on their tablet/smart-phone/laptop and enrol in some of the better and more relevant RSS feeds, to keep track.
All the big business and technology magazines have relevant sections on Data Science or Big Data: Forbe’s, Harvard Business Review, O’Reilly, ZDNet, MIT Sloan Management Review, Information Week, Wired, InfoWorld, TechCrunch (big data) and TechCrunch (data science), … Each of these has a particular perspective, which is useful in understanding their contributions. For instance, TechCrunch is a technology startup magazine whereas Forbes targets Fortune 500 companies. The articles in this class of magazines usually are good quality, although there are sometimes “commissioned” journalism or press releases for marketing.
Many technology blogs focus on Data Science. The following are listed as most popular first: KDNuggets.com, DataScienceCentral.com and its offshoot AnalyticBridge.com, Datafloq.com, AllAnalytics.com, PredictiveAnalyticsToday.com, Dataconomy.com, 101.DataScience.community, DataScienceWeekly.org. The first, KDNuggets has been in the business for almost two decades. Many of these have email and RSS subscription services and Twitter feeds. Some of these have a low signal to noise ratio so it is easy to get drowned in content. See also Quora’s “What are the best blogs for data scientists to read?” for more discussion.
There are two weekly newsletters that you should sign up to for great content in your email. The Data Science Weekly Newsletter has more of a technology orientation with, for instance, some popular machine learning content. The O’Reilly Data Newsletter is more about industry and is essential reading for anyone who wants to remain current.
Most of the blogs are also coupled with curated information sources. Other site with curated information are Resources to Learn Data Science Online and Big Data and Applications Knowledge Repository. This second one also has a good list of conferences.
A related category are the question answering sites: Quora has Data Science and Big Data channels, though many other discussions are useful too. A site more in the Slashdot style is Datatau.com.
Pinterest.com is a site that records infographics. e.g., queries for “data science” and “big data“. These are seductive, and some certainly informative. Datafloq.com also has an infographics section. Some notables here that go way beyond infographics are cheat sheets: Machine Learning Cheat Sheet and the Probability Cheat Sheet. These are handy academic references, and also a nice way to find out what you do not know.
Many sites give collections of data sets, so perhaps the most notable here are: aws.amazon.com data sets, KDNuggets.com awesome public datasets, Google’s public data directory, Quora.com large data sets, … The Internet Archive is a long running source of free digital content (books, etc.). There are many, many more such sites, especially as governments now support open data.
Finally, most terms and concepts are well explained in the Wikipedia, often with good diagrams and related discussions. As one delves into the more esoteric aspects of statistics or computer science, the quality of Wikipedia’s entries drop’s off. Wikipedia’s definition of Data Science, for instance, as “a continuation of the field data mining and predictive analytics” would be hotly contested by some, but others would find the distinctions not that important.
WikiBooks has now produced Data Science: An Introduction, which I haven’t looked at properly yet but the outline seems OK. I am skeptical of such efforts because the typical academic author has a focused speciality and a list of axes to grind … not me of course, oh no, not me 😉
The ML Bootcamp is a joint University of Warwick and Monash University programme organised by PhD students. Really great programme with all sorts of cool stuff in data science. My tutorial is Introducing Document Analysis (pdf slides). This is a “grand tour” tutorial, giving lots of examples rather then properly covering any particular theories or algorithms.
An earlier talk I gave, on a related topic, is Introduction to Text Mining (PDF slides), originally given to a business-technical audience in 2014. So this is more a motivational talk on text mining, why it is useful and why it is difficult.
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Bayesian Models 