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.
Do We Need Deep Learning in Time Series
Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”
Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time.
Darts Library for Time Series
Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts.
Forecasting Principles and Practice
Welcome to Timeseries! Today’s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices.
Prequisites for Time Series
Today's experimental episode uses sound to describe some basic ideas from time series.
This episode includes lag, seasonality, trend, noise, heteroskedasticity, decomposition, smoothing, feature engineering, and deep learning.
Orders of Magnitude
Today’s show in two parts. First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics.
Second, we introduce our new segment “Orders of Magnitude”. It’s a statistical game show in which participants must identify the true statistic hidden in a list of statistics which are off by at least an order of magnitude. Claudia and Vanessa join as our first contestants. Below are the sources of our questions.
https://en.wikipedia.org/wiki/Willis_Tower https://en.wikipedia.org/wiki/Eiffel_Tower https://en.wikipedia.org/wiki/GreatPyramidof_Giza https://en.wikipedia.org/wiki/InternationalSpaceStation Bird Statistics
Birds in the US since 2000 Causes of Bird Mortality Amounts of Data
Our statistics come from this post
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.
Agree with the comments on the co-hosts dynamic; he lectures, she plays dumb, podcast grates.