Patrick Winston was a professor of Artificial Intelligence at MIT. Having taught with great enthusiasm for over 50 years, he passed away past June.
As a speaker [Patrick] always had his audience in the palm of his hand. He put a tremendous amount of work into his lectures, and yet managed to make them feel loose and spontaneous. He wasn’t flashy, but he was compelling and direct.
I’ve written about Patrick’s MIT course on Artificial Intelligence before, as all 20+ lectures have been shared open access online on Youtube. I’ve worked through the whole course in 2017/2018, and it provided me many new insights into the inner workings of common machine learning algorithms.
Now, I stumbled upon another legacy of Patrick that has been opened up as of December 20th 2019. A lecture on “How to Speak” – where Patrick explains what he think makes a talk enticing, inspirational, and interesting.
Patrick Winston’s How to Speak talk has been an MIT tradition for over 40 years. Offered every January, the talk is intended to improve your speaking ability in critical situations by teaching you a few heuristic rules.
Josh Starmer is assistant professor at the genetics department of the University of North Carolina at Chapel Hill.
But more importantly: Josh is the mastermind behind StatQuest!
StatQuest is a Youtube channel (and website) dedicated to explaining complex statistical concepts — like data distributions, probability, or novel machine learning algorithms — in simple terms.
Once you watch one of Josh’s “Stat-Quests”, you immediately recognize the effort he put into this project. Using great visuals, a just-about-right pace, and relateable examples, Josh makes statistics accessible to everyone. For instance, take this series on logistic regression:
And do you really know what happens under the hood when you run a principal component analysis? After this video you will:
Or are you more interested in learning the fundamental concepts behind machine learning, then Josh has some videos for you, for instance on bias and variance or gradient descent:
With nearly 200 videos and counting, StatQuest is truly an amazing resource for students ‘and teachers on topics related to statistics and data analytics. For some of the concepts, Josh even posted videos running you through the analysis steps and results interpretation in the R language.
StatQuest started out as an attempt to explain statistics to my co-workers – who are all genetics researchers at UNC-Chapel Hill. They did these amazing experiments, but they didn’t always know what to do with the data they generated. That was my job. But I wanted them to understand that what I do isn’t magic – it’s actually quite simple. It only seems hard because it’s all wrapped up in confusing terminology and typically communicated using equations. I found that if I stripped away the terminology and communicated the concepts using pictures, it became easy to understand.
Over time I made more and more StatQuests and now it’s my passion on YouTube.
The magazine of Tilburg University — Univers — recently interviewed me on my PhD research on People Analytics and data-driven Human Resource management. The Dutch write-up by interviewer Ron Vaessen you can find here, but is unfortunately available in Dutch only.
I have also dedicated several blogs to more background information. A small extract on the ethics of people analytics and machine learning in HR I posted here. Those interested in visualizing survival curves like I did can see this post. Curious about the cover design, read this post.
Massachusetts Institute of Technology (MIT) hosts their entire 2010 course on artificial intelligence / machine learning by Professor Patrick Winston on YouTube. Although some parts seem already kind of dated seven years later, the videos on several evolving topics (e.g., Neural Networks) have been updated in the fall of 2015. The tutorial assignments you can find at the course website. Requirements for the course include experience with Python programming and an understanding of search algorithms (depth-first, breadth-first, uniform-cost, A*), basic probability, state estimation, the chain rule, partial derivatives, and dot products.
Below is the first, introductory lecture, which provides a short introduction to the history and concept of artificial intelligence: AI is about algorithms enabled by constraints exposed by representations that support models targeted at loops that tie together thinking, perception and action.