x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

PODCAST

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.

technology mathematics

All Episodes

12:55

[MINI] ANOVA

Data Skeptic ·

2016/08/12

Analysis of variance is a method used to evaluate differences between the two or more groups. It works by breaking down the total variance of the system into the between group variance and within group variance. We discuss this method in the context of wait times getting coffee at Starbucks.

en

23:11

Machine Learning on Images with Noisy Human-centric...

Data Skeptic ·

2016/08/05

When humans describe images, they have a reporting bias, in that the report only what they consider important. Thus, in addition to considering whether something is present in an image, one should consider whether it is also relevant to the image before labeling it. Ishan Misra joins us this week to discuss his recent paper Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels which explores a novel architecture for learning to distinguish presence and

en

14:20

[MINI] Survival Analysis

Data Skeptic ·

2016/07/29

Survival analysis techniques are useful for studying the longevity of groups of elements or individuals, taking into account time considerations and right censorship. This episode explores how survival analysis can describe marriages, in particular, using the non-parametric Cox proportional hazard model. This episode discusses some good summaries of survey data on marriage and divorce which can be found here. The python lifelines library is a good place to get started for people that want to do

en

36:32

Predictive Models on Random Data

Data Skeptic ·

2016/07/22

This week is an insightful discussion with Claudia Perlich about some situations in machine learning where models can be built, perhaps by well-intentioned practitioners, to appear to be highly predictive despite being trained on random data. Our discussion covers some novel observations about ROC and AUC, as well as an informative discussion of leakage. Much of our discussion is inspired by two excellent papers Claudia authored: Leakage in Data Mining: Formulation, Detection, and Avoidance and On

en

11:10

[MINI] Receiver Operating Characteristic (ROC) Curve

Data Skeptic ·

2016/07/15

An ROC curve is a plot that compares the trade off of true positives and false positives of a binary classifier under different thresholds. The area under the curve (AUC) is useful in determining how discriminating a model is. Together, ROC and AUC are very useful diagnostics for understanding the power of one's model and how to tune it.

en

30:02

Multiple Comparisons and Conversion Optimization

Data Skeptic ·

2016/07/08

I'm joined by Chris Stucchio this week to discuss how deliberate or uninformed statistical practitioners can derive spurious and arbitrary results via multiple comparisons. We discuss p-hacking and a variety of other important lessons and tips for proper analysis. You can enjoy Chris's writing on his blog at chrisstucchio.com and you may also like his recent talk Multiple Comparisons: Make Your Boss Happy with False Positives, Guarenteed.

en

12:00

[MINI] Leakage

Data Skeptic ·

2016/07/01

If you'd like to make a good prediction, your best bet is to invent a time machine, visit the future, observe the value, and return to the past. For those without access to time travel technology, we need to avoid including information about the future in our training data when building machine learning models. Similarly, if any other feature whose value would not actually be available in practice at the time you'd want to use the model to make a prediction, is a feature that can introduce leakage

en

36:01

Predictive Policing

Data Skeptic ·

2016/06/24

Kristian Lum (@KLdivergence) joins me this week to discuss her work at @hrdag on predictive policing. We also discuss Multiple Systems Estimation, a technique for inferring statistical information about a population from separate sources of observation. If you enjoy this discussion, check out the panel Tyranny of the Algorithm? Predictive Analytics & Human Rights which was mentioned in the episode.

en

10:32

[MINI] The CAP Theorem

Data Skeptic ·

2016/06/17

Distributed computing cannot guarantee consistency, accuracy, and partition tolerance. Most system architects need to think carefully about how they should appropriately balance the needs of their application across these competing objectives. Linh Da and Kyle discuss the CAP Theorem using the analogy of a phone tree for alerting people about a school snow day.

en

33:10

Detecting Terrorists with Facial Recognition?

Data Skeptic ·

2016/06/10

A startup is claiming that they can detect terrorists purely through facial recognition. In this solo episode, Kyle explores the plausibility of these claims.

en

10:56

[MINI] Goodhart's Law

Data Skeptic ·

2016/06/03

Goodhart's law states that "When a measure becomes a target, it ceases to be a good measure". In this mini-episode we discuss how this affects SEO, call centers, and Scrum.

en

42:43

Data Science at eHarmony

Data Skeptic ·

2016/05/27

I'm joined this week by Jon Morra, director of data science at eHarmony to discuss a variety of ways in which machine learning and data science are being applied to help connect people for successful long term relationships. Interesting open source projects mentioned in the interview include Face-parts, a web service for detecting faces and extracting a robust set of fiducial markers (features) from the image, and Aloha, a Scala based machine learning library. You can learn more about these and

en

13:38

[MINI] Stationarity and Differencing

Data Skeptic ·

2016/05/20

Mystery shoppers and fruit cultivation help us discuss stationarity - a property of some time serieses that are invariant to time in several ways. Differencing is one approach that can often convert a non-stationary process into a stationary one. If you have a stationary process, you get the benefits of many known statistical properties that can enable you to do a significant amount of inferencing and prediction.

en

23:04

Feather

Data Skeptic ·

2016/05/13

I'm joined by Wes McKinney (@wesmckinn) and Hadley Wickham (@hadleywickham) on this episode to discuss their joint project Feather. Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording, libraries are available for R and Python, making it easy for data scientists working in these languages to quickly and effectively share datasets and collaborate.

en

15:03

[MINI] Bargaining

Data Skeptic ·

2016/05/06

Bargaining is the process of two (or more) parties attempting to agree on the price for a transaction. Game theoretic approaches attempt to find two strategies from which neither party is motivated to deviate. These strategies are said to be in equilibrium with one another. The equilibriums available in bargaining depend on the the transaction mechanism and the information of the parties. Discounting (how long parties are willing to wait) has a significant effect in this process. This episode

en

29:53

deepjazz

Data Skeptic ·

2016/04/29

Deepjazz is a project from Ji-Sung Kim, a computer science student at Princeton University. It is built using Theano, Keras, music21, and Evan Chow's project jazzml. Deepjazz is a computational music project that creates original jazz compositions using recurrent neural networks trained on Pat Metheny's "And Then I Knew". You can hear some of deepjazz's original compositions on soundcloud.

en

14:58

[MINI] Auto-correlative functions and correlograms

Data Skeptic ·

2016/04/22

When working with time series data, there are a number of important diagnostics one should consider to help understand more about the data. The auto-correlative function, plotted as a correlogram, helps explain how a given observations relates to recent preceding observations. A very random process (like lottery numbers) would show very low values, while temperature (our topic in this episode) does correlate highly with recent days. See the show notes with details about Chapel Hill, NC

en

27:05

Early Identification of Violent Criminal Gang Members

Data Skeptic ·

2016/04/15

This week I spoke with Elham Shaabani and Paulo Shakarian (@PauloShakASU) about their recent paper Early Identification of Violent Criminal Gang Members (also available onarXiv). In this paper, they use social network analysis techniques and machine learning to provide early detection of known criminal offenders who are in a high risk group for committing violent crimes in the future. Their techniques outperform existing techniques used by the police. Elham and Paulo are part of the Cyber-Socio

en

11:09

[MINI] Fractional Factorial Design

Data Skeptic ·

2016/04/08

A dinner party at Data Skeptic HQ helps teach the uses of fractional factorial design for studying 2-way interactions.

en

25:21

Machine Learning Done Wrong

Data Skeptic ·

2016/04/01

Cheng-tao Chu (@chengtao_chu) joins us this week to discuss his perspective on common mistakes and pitfalls that are made when doing machine learning. This episode is filled with sage advice for beginners and intermediate users of machine learning, and possibly some good reminders for experts as well. Our discussion parallels his recent blog postMachine Learning Done Wrong. Cheng-tao Chu is an entrepreneur who has worked at many well known silicon valley companies. His paper Map-Reduce for Machine

en

599 results

Similar Podcasts

Lead With AI

Tamara Nall

Linux in the Ham Shack

Black Sparrow Media

Data Engineering Podcast

Tobias Macey

Eye On A.I.

Craig S. Smith

Reflections

Livewire Labs