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.
Long Term Time Series Forecasting
Alex Mallen, Computer Science student at the University of Washington, and Henning Lange, a Postdoctoral Scholar in Applied Math at the University of Washington, join us today to share their work "Deep Probabilistic Koopman: Long-term Time-Series Forecasting Under Periodic Uncertainties."
Fast and Frugal Time Series Forecasting
Fotios Petropoulos, Professor of Management Science at the University of Bath in The U.K., joins us today to talk about his work "Fast and Frugal Time Series Forecasting."
Causal Inference in Educational Systems
Manie Tadayon, a PhD graduate from the ECE department at University of California, Los Angeles, joins us today to talk about his work “Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach.”
Boosted Embeddings for Time Series
Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work “Boosted Embeddings for Time Series Forecasting.”
Boosted Embeddings for Time Series Forecasting
by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr
Change Point Detection in Continuous Integration Systems
David Daly, Performance Engineer at MongoDB, joins us today to discuss "The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System".
The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System
by David Daly, William Brown, Henrik Ingo, Jim O’Leary, David BradfordSocial Media
Applying k-Nearest Neighbors to Time Series
Samya Tajmouati, a PhD student in Data Science at the University of Science of Kenitra, Morocco, joins us today to discuss her work Applying K-Nearest Neighbors to Time Series Forecasting: Two New Approaches.
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
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.
Just listened to Kyle speaking with Adrian Martin about CNN and cutting edge advancements in neural nets. Love getting this insiders perspective.