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HBHL awarded five attendance scholarships for HBHL Fellows to attend the听. The School took place from September 9 to 13, 2019 in Montreal. Through presentations and interactive tutorials, participants gained knowledge and skills in convolutional neural networks, language processing, reinforcement learning and more that they can apply to their own research.

Check out what the听HBHL Fellows had to say about their experience:

The week-long school provided a deep dive into machine learning and deep learning techniques and had us thinking about the best ways to use these methods within our own big datasets. All of the talks were aimed at a broad audience, with theoretical lectures first thing in the morning, mathematical ones at midday, and computational tutorials later in the afternoon, which really helped in understanding the methods across different perspectives. The work on convolutional neural networks and reinforcement learning were of particular interest to me since these techniques have a large overlap with my current interest in using open source data to predict patterns of mind wandering and attentional states within individuals.
-Effie Pereira (read more about Effie's experience on )

听

The deep learning workshop was an excellent opportunity, both to gain a deeper understanding of how to design convolutional neural networks used in my own research, as well as learning about many other aspects of artificial intelligence. After one day of foundational machine-learning theory, each of the following 4 days focused on a different branch of deep learning, with lectures throughout mornings and afternoons and hands-on tutorials in the evenings. This intensive mix of theoretical and practical sessions provided a strong foundation which will allow me to better apply AI in my current project and future endeavours.
-Daryan Chitsaz

听

I have never attended a workshop like this before. The teaching and support was excellent. The community attending was incredible. Everyone I met was so interesting and working to solve great problems. I really liked learning something that has applications in other fields than neuroscience (safety, traffic, banking, mining, videogames, data centres, diagnosis of rare diseases). I shared a lot of discussions throughout the week about pragmatic issues and obstacles that I will need to overcome (already collected data, sex bias, ethnicity bias). I made friends with people who want to help me succeed and apply these methods to my own research. I am really excited to explore these techniques.
-Marie-Elyse Lafaille-Magnan