TopPodcast.com
Menu
  • Home
  • Top Charts
  • Top Networks
  • Top Apps
  • Top Independents
  • Top Podfluencers
  • Top Picks
    • Top Business Podcasts
    • Top True Crime Podcasts
    • Top Finance Podcasts
    • Top Comedy Podcasts
    • Top Music Podcasts
    • Top Womens Podcasts
    • Top Kids Podcasts
    • Top Sports Podcasts
    • Top News Podcasts
    • Top Tech Podcasts
    • Top Crypto Podcasts
    • Top Entrepreneurial Podcasts
    • Top Fantasy Sports Podcasts
    • Top Political Podcasts
    • Top Science Podcasts
    • Top Self Help Podcasts
    • Top Sports Betting Podcasts
    • Top Stocks Podcasts
  • Podcast News
  • About Us
  • Podcast Advertising
  • Contact
Not in our directory?
Add Show Here
Podcast Equipment
Center

toppodcastlogoOur TOPPODCAST Picks

  • Comedy
  • Crypto
  • Sports
  • News
  • Politics
  • True Crime
  • Business
  • Finance

Follow Us

toppodcastlogoStay Connected

    View Top 200 Chart
    Back to Rankings Page
    Natural Sciences

    Data Skeptic

    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.

    Advertise

    Copyright: © Creative Commons Attribution License 3.0

    • Apple Podcasts
    • Google Play
    • Spotify
    Latest Episodes:
    Do We Need Deep Learning in Time Series Jun 16, 2021
    Show notes

    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?"

    Detecting Drift Jun 11, 2021
    Show notes

    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
    Darts Library for Time Series May 31, 2021
    Show notes

    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 May 24, 2021
    Show notes

    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 May 21, 2021
    Show notes

    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 May 07, 2021
    Show notes

    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.

    Heights

    • 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

    They're Coming for Our Jobs May 03, 2021
    Show notes

    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. Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve. Today's episode is a speculative conversation about what the future may hold.

    Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today! Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.

    Mentions
    • https://squaringthestrange.wordpress.com/
    • https://twitter.com/celestiaward
    • The legendary Dr. Jorge Pérez and his work studying unicorns
    • Supernormal stimulus
    • International Society of Caricature Artists
    • Two Heads Studios
    Pandemic Machine Learning Pitfalls Apr 26, 2021
    Show notes

    Today on the show Derek Driggs, a PhD Student at the University of Cambridge. He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.

    Help us vote for the next theme of Data Skeptic!

    Vote here: https://dataskeptic.com/vote

    Flesch Kincaid Readability Tests Apr 19, 2021
    Show notes

    Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand?

    While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease.

    In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.

    For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages. The analysis Kyle describes in this episode yields the intuitively pleasing histogram below. It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.

    Fairness Aware Outlier Detection Apr 09, 2021
    Show notes

    Today on the show we have Shubhranshu Shekar, a Ph. D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.

    Previous 1 23 24 25 26 27 61 Next

    Related Podcasts

    Science Friday

    1

    Science Friday Astronomy
    Radiolab

    2

    Radiolab Documentary
    BrainStuff

    3

    BrainStuff Natural Sciences
    StarTalk with Neil deGrasse Tyson

    4

    StarTalk with Neil deGrasse Tyson Games & Hobbies
    Radiolab

    5

    Radiolab Documentary
    Stuff To Blow Your Mind

    6

    Stuff To Blow Your Mind Life Sciences
    footer-logo

    Contact Us

    Toll Free: 844-670-7747

    Links

    • Home
    • Top Charts
    • Networks
    • Apps
    • Independents Podcasts
    • Podcast Advertising
    • Podcast News
    • Contact Us
    • About Us
    • Analytics & Insights

    Stay Connected

      Privacy, Terms of Use & Our Code of Ethics Protecting Content Creators Copyrights