BI NMA 03: Stochastic Processes Panel
07/24/21 • 60 min
Episode: BI NMA 03: Stochastic Processes Panel
Pub date: 2021-07-22
Panelists:
This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
The other panels:
- First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
- Second panel, about linear systems, real neurons, and dynamic networks.
- Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
- Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
- Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.
The podcast and artwork embedded on this page are from Paul Middlebrooks, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Episode: BI NMA 03: Stochastic Processes Panel
Pub date: 2021-07-22
Panelists:
This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
The other panels:
- First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
- Second panel, about linear systems, real neurons, and dynamic networks.
- Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
- Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
- Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.
The podcast and artwork embedded on this page are from Paul Middlebrooks, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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How Climate Change Fuels Extreme Weather
Podcast: Columbia Energy Exchange (LS 50 · TOP 0.5% what is this?)
Episode: How Climate Change Fuels Extreme Weather
Pub date: 2021-07-20
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They discuss the limitations and possibilities of this kind of attribution science and why making these connections matters.
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156 | Catherine D’Ignazio on Data, Objectivity, and Bias
Podcast: Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas (LS 69 · TOP 0.05% what is this?)
Episode: 156 | Catherine D’Ignazio on Data, Objectivity, and Bias
Pub date: 2021-07-19
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How can data be biased? Isn’t it supposed to be an objective reflection of the real world? We all know that these are somewhat naive rhetorical questions, since data can easily inherit bias from the people who collect and analyze it, just as an algorithm can make biased suggestions if it’s trained on biased datasets. A better question is, how do biases creep in, and what can we do about them? Catherine D’Ignazio is an MIT professor who has studied how biases creep into our data and algorithms, and even into the expression of values that purport to protect objective analysis. We discuss examples of these processes and how to use data to make things better.
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Catherine D’Ignazio received a Master of Fine Arts from Maine College of Art and a Master of Science in Media Arts and Sciences from the MIT Media Lab. She is currently an assistant professor of Urban Science and Planning and Director of the Data+Feminism Lab at MIT. She is the co-author, with Lauren F. Klein, of the book Data Feminism.
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The podcast and artwork embedded on this page are from Sean Carroll | Wondery, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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