Interpretability
Data Skeptic7 Jan 2020

Interpretability

Interpretability

Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the decisions of models, questions of how models actually work are becoming increasingly important to ask.

Welcome to Data Skeptic Interpretability.

In this episode, Kyle interviews Christoph Molnar about his book Interpretable Machine Learning.

Thanks to our sponsor, the Gartner Data & Analytics Summit going on in Grapevine, TX on March 23 – 26, 2020. Use discount code: dataskeptic.

Music

Our new theme song is #5 by Big D and the Kids Table.

Incidental music by Tanuki Suit Riot.

Avsnitt(589)

N-Beats

N-Beats

Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. Works Mentioned: N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio https://arxiv.org/abs/1905.10437 Social Media Linkedin Twitter

12 Juli 202134min

Translation Automation

Translation Automation

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.

6 Juli 202136min

Time Series at the Beach

Time Series at the Beach

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work "Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation."

28 Juni 202123min

Automatic Identification of Outlier Galaxy Images

Automatic Identification of Outlier Galaxy Images

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior

21 Juni 202136min

Do We Need Deep Learning in Time Series

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?"

16 Juni 202129min

Detecting Drift

Detecting Drift

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. Check out Sam's IBM statistics/ML blog at: http://www.research.ibm.com/haifa/dept/vst/ML-QA.shtml

11 Juni 202127min

Darts Library for Time Series

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.

31 Maj 202125min

Forecasting Principles and Practice

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.

24 Maj 202131min

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