More features are not always better! With an increasing number of features to consider, machine learning algorithms suffer from the curse of dimensionality, as they have a wider set and often sparser coverage of examples to consider. This episode explores a real life example of this as Kyle and Linhda discuss their thoughts on purchasing a home. The curse of dimensionality was defined by Richard Bellman, and applies in several slightly nuanced cases. This mini-episode discusses how it applies on machine learning. This episode does not, however, discuss a slightly different version of the curse of dimensionality which appears in decision theoretic situations. Consider the game of chess. One must think ahead several moves in order to execute a successful strategy. However, thinking ahead another move requires a consideration of every possible move of every piece controlled, and every possible response one's opponent may take. The space of possible future states of the board grows exponentially with the horizon one wants to look ahead to. This is present in the notably useful Bellman equation.

This episode discusses video game analytics with guest Anders Drachen. The way in which people get access to games and the opportunity for game designers to ask interesting questions with data has changed quite a bit in the last two decades. Anders shares his insights about the past, present, and future of game analytics. We explore not only some of the innovations and interesting ways of examining user experience in the gaming industry, but also touch on some of the exciting opportunities for innovation that are right on the horizon. You can find more from Anders online at andersdrachen.com, and follow him on twitter @andersdrachen

A recent episode of the Skeptics Guide to the Universe included a slight rant by Dr. Novella and the rouges about a shortcoming in operating systems.  This episode explores why such a (seemingly obvious) flaw might make sense from an engineering perspective, and how data science might be the solution. In this solo episode, Kyle proposes the concept of "annoyance mining" - the idea that with proper logging and enough feedback, data scientists could be provided the right dataset from which they can detect flaws and annoyances in software and other systems and automatically detect potential bugs, flaws, and improvements which could make those systems better. As system complexity grows, it seems that an abstraction like this might be required in order to keep maintaining an effective development cycle.  This episode is a bit of a soap box for Kyle as he explores why and how we might track an appropriate amount of data to be able to make better software and systems more suited for the users.

Elizabeth Lee from CyArk joins us in this episode to share stories of the work done capturing important historical sites digitally. CyArk is a non-profit focused on using technology to preserve the world's important historic and cultural locations digitally. CyArk's founder Ben Kacyra, a pioneer in 3D capture technology, and his wife, founded CyArk after seeing the need to preserve important artifacts and locations digitally before they are lost to natural disasters, human destruction, or the passage of time. We discuss their technology, data, and site selection including the upcoming themes of locations and the CyArk 500. Elizabeth puts out the call to all listeners to share their opinions on what important sites should be included in The Cyark 500 Challenge - an effort to digitally preserve 500 of the most culturally important heritage sites within the next five years. You can Nominate a site by submitting a short form at CyArk.org Visit http://www.cyark.org/projects/ to view an immersive, interactive experience of many of the sites preserved.

Linhda and Kyle review a New York Times article titled How Your Hometown Affects Your Chances of Marriage. This article explores research about what correlates with the likelihood of being married by age 26 by county. Kyle and LinhDa discuss some of the fine points of this research and the process of identifying factors for consideration.

With the advent of algorithms capable of beating highly ranked chess players, the temptation to cheat has emmerged as a potential threat to the integrity of this ancient and complex game. Yet, there are aspects of computer play that are measurably different than human play. Dr. Kenneth Regan has developed a methodology for looking at a long series of modes and measuring the likelihood that the moves may have been selected by an algorithm. The full transcript of this episode is well annotated and has a wealth of excellent links to the things discussed. If you're interested in learning more about Dr. Regan, his homepage (Kenneth Regan), his page on wikispaces, and the amazon page of books by Kenneth W. Regan are all great resources.

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3. Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be 3σ, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

This week Noelle Sio Saldana discusses her volunteer work at Crisis Text Line - a 24/7 service that connects anyone with crisis counselors. In the episode we discuss Noelle's career and how, as a participant in the Pivotal for Good program (a partnership with DataKind), she spent three months helping find insights in the messaging data collected by Crisis Text Line. These insights helped give visibility into a number of different aspects of Crisis Text Line's services. Listen to this episode to find out how! If you or someone you know is in a moment of crisis, there's someone ready to talk to you by texting the shortcode 741741.

Have you ever wondered what is lost when you compress a song into an MP3? This week's guest Ryan Maguire did more than that. He worked on software to issolate the sounds that are lost when you convert a lossless digital audio recording into a compressed MP3 file. To complete his project, Ryan worked primarily in python using the pyo library as well as the Bregman Toolkit Ryan mentioned humans having a dynamic range of hearing from 20 hz to 20,000 hz, if you'd like to hear those tones, check the previous link. If you'd like to know more about our guest Ryan Maguire you can find his website at the previous link. To follow The Ghost in the MP3 project, please checkout their Facebook page, or on the sitetheghostinthemp3.com. A PDF of Ryan's publication quality write up can be found at this link: The Ghost in the MP3 and it is definitely worth the read if you'd like to know more of the technical details.

This episode contains converage of the 2015 Data Fest hosted at UCLA.  Data Fest is an analysis competition that gives teams of students 48 hours to explore a new dataset and present novel findings.  This year, data from Edmunds.com was provided, and students competed in three categories: best recommendation, best use of external data, and best visualization.

For our 50th episode we enduldge a bit by cooking Linhda's previously mentioned "healthy" cornbread.  This leads to a discussion of the statistical topic of overdispersion in which the variance of some distribution is larger than what one's underlying model will account for.

This episode overviews some of the fundamental concepts of natural language processing including stemming, n-grams, part of speech tagging, and th bag of words approach.

Guest Youyou Wu discuses the work she and her collaborators did to measure the accuracy of computer based personality judgments. Using Facebook "like" data, they found that machine learning approaches could be used to estimate user's self assessment of the "big five" personality traits: openness, agreeableness, extraversion, conscientiousness, and neuroticism. Interestingly, the computer-based assessments outperformed some of the assessments of certain groups of human beings. Listen to the episode to learn more. The original paper Computer-based personality judgements are more accurate than those made by humansappeared in the January 2015 volume of the Proceedings of the National Academy of Sciences (PNAS). For her benevolent Youyou recommends Private traits and attributes are predictable from digital records of human behavior by Michal Kosinski, David Stillwell, and Thore Graepel. It's a similar paper by her co-authors which looks at demographic traits rather than personality traits. And for her self-serving recommendation, Youyou has a link that I'm very excited about. You can visitApplyMagicSauce.com to see how this model evaluates your personality based on your Facebook like information. I'd love it if listeners participated in this research and shared your perspective on the results via The Data Skeptic Podcast Facebook page. I'm going to be posting mine there for everyone to see.

This episode explores how going wine testing could teach us about using markov chain monte carlo (mcmc).

This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome. Markov Chains are a useful way method for describing non-deterministic systems. They are useful for destribing the state and transition model of a stochastic system. As examples of Markov Chains, we discuss stop light signals, bowling, and text prediction systems in light of whether or not they can be described with Markov Chains.

Nicole Goebel joins us this week to share her experiences in oceanography studying phytoplankton and other aspects of the ocean and how data plays a role in that science.   We also discuss Thinkful where Nicole and I are both mentors for the Introduction to Data Science course. Last but not least, check out Nicole's blog Data Science Girl and the videos Kyle mentioned on her Youtube channel featuring one on the diversity of phytoplankton and how that changes in time and space.

This episode explores Ordinary Least Squares or OLS - a method for finding a good fit which describes a given dataset.

New York State approved the use of automated speed cameras within a specific range of schools. Tim Schmeier did an analysis of publically available data related to these cameras as part of a project at the NYC Data Science Academy. Tim's work leverages several open data sets to ask the questions: are the speed cameras succeeding in their intended purpose of increasing public safety near schools? What he found using open data may surprise you. You can read Tim's write up titled Speed Cameras: Revenue or Public Safety? on the NYC Data Science Academy blog. His original write up, reproducible analysis, and figures are a great compliment to this episode. For his benevolent recommendation, Tim suggests listeners visit Maddie's Fund - a data driven charity devoted to helping achieve and sustain a no-kill pet nation. And for his self-serving recommendation, Tim Schmeier will very shortly be on the job market. If you, your employeer, or someone you know is looking for data science talent, you can reach time at his gmail account which is timothy.schmeier at gmail dot com.

The k-means clustering algorithm is an algorithm that computes a deterministic label for a given "k" number of clusters from an n-dimensional datset.  This mini-episode explores how Yoshi, our lilac crowned amazon's biological processes might be a useful way of measuring where she sits when there are no humans around.  Listen to find out how!

Emre Sarigol joins me this week to discuss his paper Online Privacy as a Collective Phenomenon. This paper studies data collected from social networks and how the sharing behaviors of individuals can unintentionally reveal private information about other people, including those that have not even joined the social network! For the specific test discussed, the researchers were able to accurately predict the sexual orientation of individuals, even when this information was withheld during the training of their algorithm. The research produces a surprisingly accurate predictor of this private piece of information, and was constructed only with publically available data from myspace.com found on archive.org. As Emre points out, this is a small shadow of the potential information available to modern social networks. For example, users that install the Facebook app on their mobile phones are (perhaps unknowningly) sharing all their phone contacts. Should a social network like Facebook choose to do so, this information could be aggregated to assemble "shadow profiles" containing rich data on users who may not even have an account.

My quest this week is noteworthy a.i. researcher Randy Olson who joins me to share his work creating the Reddit World Map - a visualization that illuminates clusters in the reddit community based on user behavior. Randy's blog post on created the reddit world map is well complimented by a more detailed write up titled Navigating the massive world of reddit: using backbone networks to map user interests in social media. Last but not least, an interactive version of the results (which leverages Gephi) can be found here. For a benevolent recommendation, Randy suggetss people check out Seaborn - a python library for statistical data visualization. For a self serving recommendation, Randy recommends listeners visit the Data is beautiful subreddit where he's a moderator.

When dealing with dynamic systems that are potentially undergoing constant change, its helpful to describe what "state" they are in.  In many applications the manner in which the state changes from one to another is not completely predictable, thus, there is uncertainty over how it transitions from state to state.  Further, in many applications, one cannot directly observe the true state, and thus we describe such situations as partially observable state spaces.  This episode explores what this means and why it is important in the context of chess, poker, and the mood of Yoshi the lilac crowned amazon parrot.

My guest this week is Anh Nguyen, a PhD student at the University of Wyoming working in the Evolving AI lab. The episode discusses the paper Deep Neural Networks are Easily Fooled [pdf] by Anh Nguyen, Jason Yosinski, and Jeff Clune. It describes a process for creating images that a trained deep neural network will mis-classify. If you have a deep neural network that has been trained to recognize certain types of objects in images, these "fooling" images can be constructed in a way which the network will mis-classify them. To a human observer, these fooling images often have no resemblance whatsoever to the assigned label. Previous work had shown that some images which appear to be unrecognizable white noise images to us can fool a deep neural network. This paper extends the result showing abstract images of shapes and colors, many of which have form (just not the one the network thinks) can also trick the network.

This episode introduces a high level discussion on the topic of Data Provenance, with more MINI episodes to follow to get into specific topics. Thanks to listener Sara L who wrote in to point out the Data Skeptic Podcast has focused alot about using data to be skeptical, but not necessarily being skeptical of data. Data Provenance is the concept of knowing the full origin of your dataset. Where did it come from? Who collected it? How as it collected? Does it combine independent sources or one singular source? What are the error bounds on the way it was measured? These are just some of the questions one should ask to understand their data. After all, if the antecedent of an argument is built on dubious grounds, the consequent of the argument is equally dubious. For a more technical discussion than what we get into in this mini epiosode, I recommend A Survey of Data Provenance Techniques by authors Simmhan, Plale, and Gannon.

I had the change to speak with well known Sharon Hill (@idoubtit) for the first episode of 2015. We discuss a number of interesting topics including the contributions Doubtful News makes to getting scientific and skeptical information ranked highly in search results, sink holes, why earthquakes are hard to predict, and data collection about paranormal groups via the internet.