379 episodes
Data Skeptic Kyle Polich
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- Science
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4.4 • 462 Ratings
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The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
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Comp Engine
Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine.
Follow Ben on Twitter: @bendfulcher
For posts about time series analysis : @comptimeseries
comp-engine.org -
Detecting Ransomware
Nitin Pundir, PhD candidate at University Florida and works at the Florida Institute for Cybersecurity Research, comes on today to talk about his work “RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique.”
FICS Research Lab - https://fics.institute.ufl.edu/
LinkedIn - https://www.linkedin.com/in/nitin-pundir470/ -
GANs in Finance
Florian Eckerli, a recent graduate of Zurich University of Applied Sciences, comes on the show today to discuss his work Generative Adversarial Networks in Finance: An Overview.
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Predicting Urban Land Use
Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.
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Opportunities for Skillful Weather Prediction
Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site.
Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost!
In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it’s normally chaotic state space. -
Predicting Stock Prices
Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction.
We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.
Customer Reviews
Professional and informative
The thing I like most about this podcast is the professionalism - the host, Kyle, always keep the content professional and impartial, allowing the listener to focus on the science rather than lecturing the listener. The content is also Dell big technically to serve as an introduction to many methods in ML
Great podcast
I also want to leave a five star review to counterbalance the fool who left a one star review because the host has not made a comment about BLM! This is a podcast about machine learning and AI. I’m grateful for this excellent resource and the huge amount of work that obviously goes into it.
Excellent resource!
Just listened to Kyle speaking with Adrian Martin about CNN and cutting edge advancements in neural nets. Love getting this insiders perspective.
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