Brain gains. Brain gains. Brain gains.
This is an expression often used by my good friend and colleague Josue Nasser (side note: follow him on Twitter, he’s a great human being and he works on cool stuff) that generally refers to “the practice of accumulating knowledge”. Let me tell you about some recent brain gains I’ve made.
In the past two weeks, I participated in the Machine Learning Summer School (MLSS) in London, organized by the amazing duo of Marc Deisenroth and Arthur Gretton. Seriously, these guys are the pinnacle of awesomeness.
This event stands as one of the most positive experiences of my graduate student career. Every morning, afternoon, and night was filled with major brain gains. There were academic brain gains that allowed me to build upon my general understanding of machine learning methods, but more importantly, there were also brain gains I made as a human being; brain gains that taught me the importance of fostering a diverse, well-represented, and inclusive community in machine learning and BEYOND.
Let me give you an overview of my experience.
The agenda of the MLSS included a set of sessions on a wide variety of machine learning topics such as: variational inference, optimization, deep learning, reinforcement learning, interpretability, Gaussian processes, AI for good, kernels, MCMC, ABC, fairness, speech processing, learning theory, ML in computational biology, and submodularity.
Most sessions began with about 3-4 hours of lecture material followed by a practical, which allowed us to see some of the discussed theory and algorithms applied in practice.
The sessions were valuable. They were structured in a way as to provide each participant with a sufficient amount of knowledge about a variety of subfields in machine learning. This knowledge was extremely useful for someone like me, who studies a topic as specific as Monte Carlo methods and had little knowledge about things like reproducing kernel Hilbert spaces (shout out to Lorenzo Rosasco for being an awesome kernels teacher).
Between the lectures and tutorials, we were in the classroom from about 9AM until 7PM each day (with some breaks for coffee and lunch). Following the sessions, we usually had some sort of event (poster session, pizza night, or reception), where participants could interact with the instructors, industry professionals, and of course, each other. Usually, I would make it back to the hostel at about 10-11PM everyday.
This was extremely tiring. Had I been alone, I probably would not have been as productive as I was. But that’s the thing, I was not alone.
During the MLSS, I was surrounded by an amazing group of newly made friends and colleagues that, to me, represent the brightest of futures for the machine learning community. I had the opportunity to meet and interact with students from all over the world, whose nationalities represented over 50 countries.
Kudos to the organizers for bringing together such a diverse international community. For someone like me who comes from the US, it was an extremely humbling and rewarding experience to be able to meet this great group of students. Check us all out:
I want to end now by saying my thanks.
Thank you to the organizers, Marc and Arthur. You two represent everything great about the machine learning community. I wholeheartedly believe that your efforts are helping to shape a positive future for my generation; one that emphasizes positivity, diversity, and inclusion. For that, I am so thankful.
Thank you the instructors and volunteers for your efforts in preparing the lecture materials and tutorial sessions.
Thank you to the other participants for your friendship, acceptance, and support over the past two weeks. I know I will meet many of you again in the near future.
This summer school was more than I could have hoped for and I know that I have you all to thank for it!
For bonus material, checkout this slideshow of some glamorus (and not so glamorous) photos:
Check out other blog posts about the event here:
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