Data Skeptic

In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's latest and most exciting innovations in AI development platforms. Last month, Microsoft launched a set of three powerful new capabilities in Azure Machine Learning for advanced developers to exploit big data, GPUs, data wrangling and container-based model deployment.

Extended show notes found here.

Thanks to our sponsor Springboard.  Check out Springboard's Data Science Career Track Bootcamp.

Direct download: data-science-tools-and-other-announcements-from-ignite.mp3
Category:data science -- posted at: 8:00am PDT

Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence using neural networks. More specifically, it was authored by a recurrent neural network (RNN) called long short-term memory (LSTM). According to End Cue’s Chief Technical Officer, Deb Ray, the company has come a long way in improving the generative AI aspect of the bot. In this episode, Deb Ray joins host Kyle Polich to discuss how generative AI models are being applied in creative processes, such as screenwriting. Their discussion also explores how data science for analyzing development projects, such as financing and selecting scripts, as well as optimizing the content production process.

Direct download: generative-ai-for-content-creation.mp3
Category:data science -- posted at: 8:00am PDT

One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples.  This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model.

In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each.  Why can the human brain recognize a new symbol with relative ease while most machine learning algorithms require large training data?  We discuss some of the reasons why and approaches to One Shot Learning.

Direct download: one-shot-learning.mp3
Category:general -- posted at: 8:00am PDT

Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the unique challenges that recommender systems face. In this episode, host Kyle Polich joins Dr. Joseph Konstan from the University of Minnesota at a live recording at FARCON 2017 in Minneapolis to discuss recommender systems and how machine learning can create better user experiences. 

Direct download: recommender-systems-live-from-farcon.mp3
Category:general -- posted at: 8:00am PDT

Thanks to our sponsor brilliant.org/dataskeptics

A Long Short Term Memory (LSTM) is a neural unit, often used in Recurrent Neural Network (RNN) which attempts to provide the network the capacity to store information for longer periods of time. An LSTM unit remembers values for either long or short time periods. The key to this ability is that it uses no activation function within its recurrent components. Thus, the stored value is not iteratively modified and the gradient does not tend to vanish when trained with backpropagation through time.

Direct download: long-short-term-memory.mp3
Category:general -- posted at: 8:00am PDT

Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn more about how Zillow uses data science and big data to make real estate predictions.

Direct download: zillow-zestimate.mp3
Category:general -- posted at: 8:00am PDT

Our guest Pranav Rajpurkar and his coauthored recently published Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, a paper in which they demonstrate the use of Convolutional Neural Networks which outperform board certified cardiologists in detecting a wide range of heart arrhythmias from ECG data.

Direct download: cardiologist-level-arrhythmia-detection-with-cnns.mp3
Category:general -- posted at: 8:00am PDT

RNNs are a class of deep learning models designed to capture sequential behavior.  An RNN trains a set of weights which depend not just on new input but also on the previous state of the neural network.  This directed cycle allows the training phase to find solutions which rely on the state at a previous time, thus giving the network a form of memory.  RNNs have been used effectively in language analysis, translation, speech recognition, and many other tasks.

Direct download: recurrent-neural-networks.mp3
Category:general -- posted at: 8:00am PDT

Thanks to our sponsor Springboard.

In this week's episode, guest Andre Natal from Mozilla joins our host, Kyle Polich, to discuss a couple exciting new developments in open source speech recognition systems, which include Project Common Voice.

In June 2017, Mozilla launched a new open source project, Common Voice, a novel complementary project to the TensorFlow-based DeepSpeech implementation. DeepSpeech is a deep learning-based voice recognition system that was designed by Baidu, which they describe in greater detail in their research paper. DeepSpeech is a speech-to-text engine, and Mozilla hopes that, in the future, they can use Common Voice data to train their DeepSpeech engine.

Direct download: project-common-voice.mp3
Category:general -- posted at: 8:00am PDT

A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between them. It's an intuitive way of encoding your statistical knowledge about a system and is efficient to propagate belief updates throughout the network when new information is added.

Direct download: bayesian-belief-networks.mp3
Category:general -- posted at: 11:58pm PDT

In this episode, Tony Beltramelli of UIzard Technologies joins our host, Kyle Polich, to talk about the ideas behind his latest app that can transform graphic design into functioning code, as well as his previous work on spying with wearables.

Direct download: pix2code.mp3
Category:general -- posted at: 8:00am PDT

In statistics, two random variables might depend on one another (for example, interest rates and new home purchases). We call this conditional dependence. An important related concept exists called conditional independence. This phrase describes situations in which two variables are independent of one another given some other variable.

For example, the probability that a vendor will pay their bill on time could depend on many factors such as the company's market cap. Thus, a statistical analysis would reveal many relationships between observable details about the company and their propensity for paying on time. However, if you know that the company has filed for bankruptcy, then we might assume their chances of paying on time have dropped to near 0, and the result is now independent of all other factors in light of this new information.

We discuss a few real world analogies to this idea in the context of some chance meetings on our recent trip to New York City.

Direct download: conditional-independence.mp3
Category:general -- posted at: 8:00am PDT

Animals can't tell us when they're experiencing pain, so we have to rely on other cues to help treat their discomfort. But it is often difficult to tell how much an animal is suffering. The sheep, for instance, is the most inscrutable of animals. However, scientists have figured out a way to understand sheep facial expressions using artificial intelligence.

On this week's episode, Dr. Marwa Mahmoud from the University of Cambridge joins us to discuss her recent study, "Estimating Sheep Pain Level Using Facial Action Unit Detection." Marwa and her colleague's at Cambridge's Computer Laboratory developed an automated system using machine learning algorithms to detect and assess when a sheep is in pain. We discuss some details of her work, how she became interested in studying sheep facial expression to measure pain, and her future goals for this project.

If you're able to be in Minneapolis, MN on August 23rd or 24th, consider attending Farcon. Get your tickets today via https://farcon2017.eventbrite.com.

Direct download: estimating-sheep-pain-with-facial-recognition.mp3
Category:general -- posted at: 8:00am PDT

This episode collects interviews from my recent trip to Microsoft Build where I had the opportunity to speak with Dharma Shukla and Syam Nair about the recently announced CosmosDB. CosmosDB is a globally consistent, distributed datastore that supports all the popular persistent storage formats (relational, key/value pair, document database, and graph) under a single streamlined API. The system provides tunable consistency, allowing the user to make choices about how consistency trade-offs are managed under the hood, if a consumer wants to go beyond the selected defaults.

Direct download: cosmosdb.mp3
Category:general -- posted at: 8:00am PDT

This episode discusses the vanishing gradient - a problem that arises when training deep neural networks in which nearly all the gradients are very close to zero by the time back-propagation has reached the first hidden layer. This makes learning virtually impossible without some clever trick or improved methodology to help earlier layers begin to learn.

Direct download: the-vanishing-gradient.mp3
Category:general -- posted at: 8:00am PDT

hen faced with medical issues, would you want to be seen by a human or a machine? In this episode, guest Edward Choi, co-author of the study titled Doctor AI: Predicting Clinical Events via Recurrent Neural Network shares his thoughts. Edward presents his team’s efforts in developing a temporal model that can learn from human doctors based on their collective knowledge, i.e. the large amount of Electronic Health Record (EHR) data.

Direct download: doctor-ai.mp3
Category:general -- posted at: 8:00am PDT

In a neural network, the output value of a neuron is almost always transformed in some way using a function. A trivial choice would be a linear transformation which can only scale the data. However, other transformations, like a step function allow for non-linear properties to be introduced.

Activation functions can also help to standardize your data between layers. Some functions such as the sigmoid have the effect of "focusing" the area of interest on data. Extreme values are placed close together, while values near it's point of inflection change more quickly with respect to small changes in the input. Similarly, these functions can take any real number and map all of them to a finite range such as [0, 1] which can have many advantages for downstream calculation.

In this episode, we overview the concept and discuss a few reasons why you might select one function verse another.

Direct download: activation-functions.mp3
Category:general -- posted at: 8:00am PDT

This episode recaps the Microsoft Build Conference.  Kyle recently attended and shares some thoughts on cloud, databases, cognitive services, and artificial intelligence.  The episode includes interviews with Rohan Kumar and David Carmona.

 

Direct download: ms-build-recap.mp3
Category:general -- posted at: 8:00am PDT

Max-pooling is a procedure in a neural network which has several benefits. It performs dimensionality reduction by taking a collection of neurons and reducing them to a single value for future layers to receive as input. It can also prevent overfitting, since it takes a large set of inputs and admits only one value, making it harder to memorize the input. In this episode, we discuss the intuitive interpretation of max-pooling and why it's more common than mean-pooling or (theoretically) quartile-pooling.

Direct download: max-pooling.mp3
Category:general -- posted at: 8:00am PDT

This episode is an interview with Tinghui Zhou.  In the recent paper "Unsupervised Learning of Depth and Ego-motion from Video", Tinghui and collaborators propose a deep learning architecture which is able to learn depth and pose information from unlabeled videos.  We discuss details of this project and its applications.

Direct download: unsupervised-depth-perception.mp3
Category:general -- posted at: 8:00am PDT

CNNs are characterized by their use of a group of neurons typically referred to as a filter or kernel.  In image recognition, this kernel is repeated over the entire image.  In this way, CNNs may achieve the property of translational invariance - once trained to recognize certain things, changing the position of that thing in an image should not disrupt the CNN's ability to recognize it.  In this episode, we discuss a few high-level details of this important architecture.

Direct download: cnns.mp3
Category:general -- posted at: 8:00am PDT

Despite the success of GANs in imaging, one of its major drawbacks is the problem of 'mode collapse,' where the generator learns to produce samples with extremely low variety.

To address this issue, today's guests Arnab Ghosh and Viveka Kulharia proposed two different extensions. The first involves tweaking the generator's objective function with a diversity enforcing term that would assess similarities between the different samples generated by different generators. The second comprises modifying the discriminator objective function, pushing generations corresponding to different generators towards different identifiable modes.

Direct download: mutli-agent-diverse-generative-adversarial-networks.mp3
Category:general -- posted at: 8:00am PDT

GANs are an unsupervised learning method involving two neural networks iteratively competing. The discriminator is a typical learning system. It attempts to develop the ability to recognize members of a certain class, such as all photos which have birds in them. The generator attempts to create false examples which the discriminator incorrectly classifies. In successive training rounds, the networks examine each and play a mini-max game of trying to harm the performance of the other.

In addition to being a useful way of training networks in the absence of a large body of labeled data, there are additional benefits. The discriminator may end up learning more about edge cases than it otherwise would be given typical examples. Also, the generator's false images can be novel and interesting on their own.

The concept was first introduced in the paper Generative Adversarial Networks.

Direct download: generative-adversarial-networks.mp3
Category:general -- posted at: 8:00am PDT

Recently, we've seen opinion polls come under some skepticism.  But is that skepticism truly justified?  The recent Brexit referendum and US 2016 Presidential Election are examples where some claims the polls "got it wrong".  This episode explores this idea.

Direct download: polling.mp3
Category:general -- posted at: 8:00am PDT

No reliable, complete database cataloging home sales data at a transaction level is available for the average person to access. To a data scientist interesting in studying this data, our hands are complete tied. Opportunities like testing sociological theories, exploring economic impacts, study market forces, or simply research the value of an investment when buying a home are all blocked by the lack of easy access to this dataset. OpenHouse seeks to correct that by centralizing and standardizing all publicly available home sales transactional data. In this episode, we discuss the achievements of OpenHouse to date, and what plans exist for the future.

Check out the OpenHouse gallery.

I also encourage everyone to check out the project Zareen mentioned which was her Harry Potter word2vec webapp and Joy's project doing data visualization on Jawbone data.

Guests

Thanks again to @iamzareenf, @blueplastic, and @joytafty for coming on the show. Thanks to the numerous other volunteers who have helped with the project as well!

Announcements and details

Sponsor

Thanks to our sponsor for this episode Periscope Data. The blog post demoing their maps option is on our blog titled Periscope Data Maps.

Periscope Data

To start a free trial of their dashboarding too, visit http://periscopedata.com/skeptics

Kyle recently did a youtube video exploring the Data Skeptic podcast download numbers using Periscope Data. Check it out at https://youtu.be/aglpJrMp0M4.

Supplemental music is Lee Rosevere's Let's Start at the Beginning.

 

Direct download: openhouse.mp3
Category:general -- posted at: 8:30am PDT

There's more than one type of computer processor. The central processing unit (CPU) is typically what one means when they say "processor". GPUs were introduced to be highly optimized for doing floating point computations in parallel. These types of operations were very useful for high end video games, but as it turns out, those same processors are extremely useful for machine learning. In this mini-episode we discuss why.

Direct download: gpu-cpu.mp3
Category:general -- posted at: 8:00am PDT

Backpropagation is a common algorithm for training a neural network.  It works by computing the gradient of each weight with respect to the overall error, and using stochastic gradient descent to iteratively fine tune the weights of the network.  In this episode, we compare this concept to finding a location on a map, marble maze games, and golf.

Direct download: backpropagation.mp3
Category:general -- posted at: 8:00am PDT

 

In this week's episode of Data Skeptic, host Kyle Polich talks with guest Maura Church, Patreon's data science manager. Patreon is a fast-growing crowdfunding platform that allows artists and creators of all kinds build their own subscription content service. The platform allows fans to become patrons of their favorite artists- an idea similar the Renaissance times, when musicians would rely on benefactors to become their patrons so they could make more art. At Patreon, Maura's data science team strives to provide creators with insight, information, and tools, so that creators can focus on what they do best-- making art.

On the show, Maura talks about some of her projects with the data science team at Patreon. Among the several topics discussed during the episode include: optical music recognition (OMR) to translate musical scores to electronic format, network analysis to understand the connection between creators and patrons, growth forecasting and modeling in a new market, and churn modeling to determine predictors of long time support.

A more detailed explanation of Patreon's A/B testing framework can be found here

Other useful links to topics mentioned during the show:

OMR research

Patreon blog

Patreon HQ blog

Amanda Palmer

Fran Meneses

Direct download: data-science-at-patreon.mp3
Category:general -- posted at: 8:00am PDT

Feed Forward Neural Networks

In a feed forward neural network, neurons cannot form a cycle. In this episode, we explore how such a network would be able to represent three common logical operators: OR, AND, and XOR. The XOR operation is the interesting case.

Below are the truth tables that describe each of these functions.

AND Truth Table

Input 1 Input 2 Output
0 0 0
0 1 0
1 0 0
1 1 1

OR Truth Table

Input 1 Input 2 Output
0 0 0
0 1 1
1 0 1
1 1 1

XOR Truth Table

Input 1 Input 2 Output
0 0 0
0 1 1
1 0 1
1 1 0

The AND and OR functions should seem very intuitive. Exclusive or (XOR) if true if and only if exactly single input is 1. Could a neural network learn these mathematical functions?

Let's consider the perceptron described below. First we see the visual representation, then the Activation function A, followed by the formula for calculating the output.

 

 

Output = A(w_0 \cdot Bias + w_1 \cdot Input_1 + w_2 \cdot Input_2)

 

 

Can this perceptron learn the AND function?

Sure. Let w_0 = -0.6 and w_1 = w_2 = 0.5

What about OR?

Yup. Let w_0 = 0 and w_1 = w_2 = 0.5

An infinite number of possible solutions exist, I just picked values that hopefully seem intuitive. This is also a good example of why the bias term is important. Without it, the AND function could not be represented.

How about XOR?

No. It is not possible to represent XOR with a single layer. It requires two layers. The image below shows how it could be done with two laters.

 

 

In the above example, the weights computed for the middle hidden node capture the essence of why this works. This node activates when recieving two positive inputs, thus contributing a heavy penalty to be summed by the output node. If a single input is 1, this node will not activate.

Universal approximation theorem tells us that any continuous function can be tightly approximated using a neural network with only a single hidden layer and a finite number of neurons. With this in mind, a feed forward neural network should be adaquet for any applications. However, in practice, other network architectures and the allowance of more hidden layers are empirically motivated.

Other types neural networks have less strict structal definitions. The various ways one might relax this constraint generate other classes of neural networks that often have interesting properties. We'll get into some of these in future mini-episodes.

 

Periscope Data

Check out our recent blog post on how we're using Periscope Data cohort charts.

Thanks to Periscope Data for sponsoring this episode. More about them at periscopedata.com/skeptics

Direct download: feed-forward-neural-networks.mp3
Category:general -- posted at: 8:00am PDT

In this Data Skeptic episode, Kyle is joined by guest Ruggiero Cavallo to discuss his latest efforts to mitigate the problems presented in this new world of online advertising. Working with his collaborators, Ruggiero reconsiders the search ad allocation and pricing problems from the ground up and redesigns a search ad selling system. He discusses a mechanism that optimizes an entire page of ads globally based on efficiency-maximizing search allocation and a novel technical approach to computing prices.

Direct download: reinventing-sponsored-search-auctions.mp3
Category:general -- posted at: 8:00am PDT

Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates its weights after seeing every example, rather than as a batch. It uses a step function as an activation function. It's only appropriate for linearly separable data, and it will converge to a solution if the data meets these criteria. Being a fairly simple algorithm, it can run very efficiently. Although we don't discuss it in this episode, multi-layer perceptron networks are what makes this technique most attractive.

Direct download: the-perceptron.mp3
Category:general -- posted at: 8:00am PDT

DataRefuge is a public collaborative, grassroots effort around the United States in which scientists, researchers, computer scientists, librarians and other volunteers are working to download, save, and re-upload government data. The DataRefuge Project, which is led by the UPenn Program in Environmental Humanities and the Penn Libraries group at University of Pennsylvania, aims to foster resilience in an era of anthropogenic global climate change and raise awareness of how social and political events affect transparency.

 

Direct download: data-refuge.mp3
Category:general -- posted at: 8:00am PDT

If a CEO wants to know the state of their business, they ask their highest ranking executives. These executives, in turn, should know the state of the business through reports from their subordinates. This structure is roughly analogous to a process observed in deep learning, where each layer of the business reports up different types of observations, KPIs, and reports to be interpreted by the next layer of the business. In deep learning, this process can be thought of as automated feature engineering. DNNs built to recognize objects in images may learn structures that behave like edge detectors in the first hidden layer. Proceeding layers learn to compose more abstract features from lower level outputs. This episode explore that analogy in the context of automated feature engineering.

Linh Da and Kyle discuss a particular image in this episode. The image included below in the show notes is drawn from the work of Lee, Grosse, Ranganath, and Ng in their paper Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.

 

Direct download: automated-feature-engineering.mp3
Category:general -- posted at: 8:00am PDT

In this episode, I speak with Raghu Ramakrishnan, CTO for Data at Microsoft.  We discuss services, tools, and developments in the big data sphere as well as the underlying needs that drove these innovations.

Direct download: big-data-tools-and-trends.mp3
Category:general -- posted at: 9:28am PDT

In this episode, we talk about a high-level description of deep learning.  Kyle presents a simple game (pictured below), which is more of a puzzle really, to try and give  Linh Da the basic concept.

 

 

Thanks to our sponsor for this week, the Data Science Association. Please check out their upcoming Dallas conference at dallasdatascience.eventbrite.com

Direct download: deep-learning-primer.mp3
Category:general -- posted at: 8:00am PDT

Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and reproducibility. Daniel Whitenack joins me this week to talk about these concepts and share his work on Pachyderm. Pachyderm is an open source containerized data lake.

During the show, Daniel mentioned the Gopher Data Science github repo as a great resource for any data scientists interested in the Go language. Although we didn't mention it, Daniel also did an interesting analysis on the 2016 world chess championship that complements our recent episode on chess well. You can find that post here

Supplemental music is Lee Rosevere's Let's Start at the Beginning.

 

Thanks to Periscope Data for sponsoring this episode. More about them at periscopedata.com/skeptics

Periscope Data

 

 

 

Direct download: data-provenance-and-reproducibility-with-pachyderm.mp3
Category:general -- posted at: 9:09am PDT

Logistic Regression is a popular classification algorithm. In this episode, we discuss how it can be used to determine if an audio clip represents one of two given speakers. It assumes an output variable (isLinhda) is a linear combination of available features, which are spectral bands in the discussion on this episode.

 

Keep an eye on the dataskeptic.com blog this week as we post more details about this project.

 

Thanks to our sponsor this week, the Data Science Association.  Please check out their upcoming conference in Dallas on Saturday, February 18th, 2017 via the link below.

 

dallasdatascience.eventbrite.com

 

Direct download: logistic-regression.mp3
Category:general -- posted at: 9:08am PDT

Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas such as labor market outcomes. A well structured study is challenging due to numerous confounding factors. Peter Backus and his colleagues have identified competitive chess as an ideal arena to study the topic. Find out why and what conclusions they reached.

Our discussion centers around Gender, Competition and Performance: Evidence from Real Tournaments from Backus, Cubel, Guid, Sanchez-Pages, and Mañas. A summary of their paper can also be found here.

 

Direct download: studying-competition-and-gender-through-chess.mp3
Category:general -- posted at: 8:00am PDT

Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to converge. One technique for fighting overfitting is to use dropout. Dropout is the method of randomly selecting some neurons in one's network to set to zero during iterations of learning. The core idea is that each particular input in a given layer is not always available and therefore not a signal that can be relied on too heavily.

 

Direct download: dropout.mp3
Category:general -- posted at: 7:56am PDT

In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven Justice Initiative respectively.

The Police Data Initiative was organized to use open data to increase transparency and community trust as well as to help police agencies use data for internal accountability. The PDI emerged from recommendations made by the Task Force on 21st Century Policing.

The Data Driven Justice Initiative was organized to help city, county, and state governments use data-driven strategies to help low-level offenders with mental illness get directed to the right services rather than into the criminal justice system.

Direct download: police-data-initiative-and-data-driven-justice-initiative.mp3
Category:general -- posted at: 8:10am PDT

We close out 2016 with a discussion of a basic interview question which might get asked when applying for a data science job. Specifically, how a library might build a model to predict if a book will be returned late or not.

 
Direct download: the-library-problem.mp3
Category:general -- posted at: 8:07am PDT

Today's episode is a reading of Isaac Asimov's Franchise.  As mentioned on the show, this is just a work of fiction to be enjoyed and not in any way some obfuscated political statement.  Enjoy, and happy holidays!

Direct download: 2016-holiday-special.mp3
Category:general -- posted at: 8:00am PDT

Classically, entropy is a measure of disorder in a system. From a statistical perspective, it is more useful to say it's a measure of the unpredictability of the system. In this episode we discuss how information reduces the entropy in deciding whether or not Yoshi the parrot will like a new chew toy. A few other everyday examples help us examine why entropy is a nice metric for constructing a decision tree.

Direct download: entropy.mp3
Category:general -- posted at: 12:23am PDT

Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few other cloud topics.

Direct download: ms-connect-conference.mp3
Category:general -- posted at: 8:24am PDT

Today's episode is all about Causal Impact, a technique for estimating the impact of a particular event on a time series. We talk to William Martin about his research into the impact releases have on app and we also chat with Karen Blakemore about a project she helped us build to explore the impact of a Saturday Night Live appearance on a musician's career.

Martin's work culminated in a paper Causal Impact for App Store Analysis. A shorter summary version can be found here. His company helping app developers do this sort of analysis can be found at crestweb.cs.ucl.ac.uk/appredict/.

Direct download: causal-impact.mp3
Category:general -- posted at: 7:56am PDT

The Bootstrap is a method of resampling a dataset to possibly refine it's accuracy and produce useful metrics on the result. The bootstrap is a useful statistical technique and is leveraged in Bagging (bootstrap aggregation) algorithms such as Random Forest. We discuss this technique related to polling and surveys.

Direct download: the-bootstrap.mp3
Category:general -- posted at: 8:00am PDT

The Gini Coefficient (as it relates to decision trees) is one approach to determining the optimal decision to introduce which splits your dataset as part of a decision tree. To pick the right feature to split on, it considers the frequency of the values of that feature and how well the values correlate with specific outcomes that you are trying to predict.

Direct download: gini-index.mp3
Category:general -- posted at: 8:00am PDT

Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions.

Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here and all related code is available on github.

Direct download: unstructured-data-for-finance.mp3
Category:general -- posted at: 8:00am PDT

AdaBoost is a canonical example of the class of AnyBoost algorithms that create ensembles of weak learners. We discuss how a complex problem like predicting restaurant failure (which is surely caused by different problems in different situations) might benefit from this technique.

Direct download: adaboost.mp3
Category:general -- posted at: 8:00am PDT

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered?

Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.

Direct download: stealing-models-from-the-cloud.mp3
Category:general -- posted at: 7:54am PDT

For machine learning models created with the random forest algorithm, there is no obvious diagnostic to inform you which features are more important in the output of the model. Some straightforward but useful techniques exist revolving around removing a feature and measuring the decrease in accuracy or Gini values in the leaves. We broadly discuss these techniques in this episode.

Direct download: feature-importance.mp3
Category:general -- posted at: 9:24am PDT

As cities provide bike sharing services, they must also plan for how to redistribute bicycles as they inevitably build up at more popular destination stations. In this episode, Hui Xiong talks about the solution he and his colleagues developed to rebalance bike sharing systems.

Direct download: nyc-bike-share-rebalancing.mp3
Category:general -- posted at: 8:00am PDT

Random forest is a popular ensemble learning algorithm which leverages bagging both for sampling and feature selection. In this episode we make an analogy to the process of running a bookstore.

Direct download: random-forest.mp3
Category:general -- posted at: 8:00am PDT

Jo Hardin joins us this week to discuss the ASA's Election Prediction Contest. This is a competition aimed at forecasting the results of the upcoming US presidential election competition. More details are available in Jo's blog post found here.

You can find some useful R code for getting started automatically gathering data from 538 via Jo's github and official contest details are available here. During the interview we also mention Daily Kos and 538.

Direct download: asa-election-prediction-with-jo-hardin.mp3
Category:general -- posted at: 8:00am PDT

The F1 score is a model diagnostic that combines precision and recall to provide a singular evaluation for model comparison.  In this episode we discuss how it applies to selecting an interior designer.

Direct download: f1-score.mp3
Category:general -- posted at: 9:49am PDT

Urban congestion effects every person living in a city of any reasonable size. Lewis Lehe joins us in this episode to share his work on downtown congestion pricing. We explore topics of how different pricing mechanisms effect congestion as well as how data visualization can inform choices.

You can find examples of Lewis's work at setosa.io. His paper which we discussed during the interview isDistance-dependent congestion pricing for downtown zones.

On this episode, we discuss State of California data which can be found at pems.dot.ca.gov.

Direct download: urban-congestion.mp3
Category:general -- posted at: 8:00am PDT

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range.  For example, the variance in the length of a cat's tail almost certainly changes (grows) with age.  On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

We also discuss some issues with the visualization shown in the tweet embedded below.

Image claiming relationship between income and tickets issued

Direct download: heteroskedasticity.mp3
Category:general -- posted at: 8:00am PDT

Our guest today is Michael Cuthbert, an associate professor of music at MIT and principal investigator of the Music21 project, which we focus our discussion on today.

Music21 is a python library making analysis of music accessible and fun. It supports integration with popular formats such as MIDI, MusicXML, Lilypond, and others. It's also well integrated with The Elvis Project, enabling users to import large volumes of music for easy analysis. Music21 is a great platform for musicologists and machine learning researchers alike to explore patterns and structure in music.

Direct download: music21.mp3
Category:general -- posted at: 8:00am PDT

Paxos is a protocol for arriving a consensus in a distributed computing system which accounts for unreliability of the nodes.  We discuss how this might be used in the real world in the event of a massive disaster.

Direct download: paxos.mp3
Category:general -- posted at: 8:00am PDT

Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems.

The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of the example are likely to reveal the relevant features in the local input space to reveal details about why the model arrives at it's conclusion.

In this episode, Marco Tulio Ribeiro joins us to discuss how LIME (Locally Interpretable Model-Agnostic Explanations) can help users trust machine learning models. The accompanying paper is titled "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Direct download: trust-in-ml.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: anova.mp3
Category:general -- posted at: 8:00am PDT

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 relevance. This work enables web-scale datasets to be useful for training, not just well groomed hand labeled corpora.

Direct download: ishan.mp3
Category:general -- posted at: 8:00am PDT

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 some hands on work.

Direct download: survival-analysis.mp3
Category:general -- posted at: 8:00am PDT

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 Cross Validation and Stacking: Building Seemingly Predictive Models on Random Data. Both are highly recommended reading!

Direct download: Predictive_Models_on_Random_Data.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: roc-auc.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: multiple-comparisons.mp3
Category:general -- posted at: 8:00am PDT

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 to your model.

Direct download: leakage.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: predictive-policing.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: cap-theorem.mp3
Category:general -- posted at: 8:00am PDT

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

Direct download: detecting-terrorists.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: goodharts-law.mp3
Category:general -- posted at: 8:00am PDT

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 other interesting projects at the eHarmony github page.

In the wrap up, Jon mentioned the LA Machine Learning meetup which he runs. This is a great resource for LA residents separate and complementary to datascience.la groups, so consider signing up for all of the above and I hope to see you there in the future.

Direct download: data-science-at-eharmony.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: stationarity.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: feather.mp3
Category:general -- posted at: 8:00am PDT

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 discusses some of the choices Kyle and Linh Da made in deciding what offer to make on a house.

Direct download: bargaining.mp3
Category:general -- posted at: 8:00am PDT

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.

Direct download: deepjazz.mp3
Category:general -- posted at: 8:00am PDT

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 weather data by visiting:
 
 
Direct download: acf.mp3
Category:general -- posted at: 8:00am PDT

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 Intelligent Systems (CySIS) Lab.

Direct download: predicting-violent-offenders.mp3
Category:general -- posted at: 8:00am PDT

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

Direct download: Fractional_factorial_design.mp3
Category:general -- posted at: 8:00am PDT

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 Learning on Multicore is the basis for Apache Mahout. His most recent endeavor has just emerged from steath, so please check out OneInterview.io.

Direct download: machine_learning_done_wrong.mp3
Category:general -- posted at: 8:00am PDT

Co-host Linh Da was in a biking accident after hitting a pothole. She sustained an injury that required stitches. This is the story of our quest to file a 311 complaint and track it through the City of Los Angeles's open data portal.

My guests this episode are Chelsea Ursaner (LA City Open Data Team), Ben Berkowitz (CEO and founder of SeeClickFix), and Russ Klettke (Editor of pothole.info)

Direct download: potholes.mp3
Category:general -- posted at: 8:24am PDT

Certain data mining algorithms (including k-means clustering and k-nearest neighbors) require a user defined parameter k. A user of these algorithms is required to select this value, which raises the questions: what is the "best" value of k that one should select to solve their problem?

This mini-episode explores the appropriate value of k to use when trying to estimate the cost of a house in Los Angeles based on the closests sales in it's area.

Direct download: the-elbow-method.mp3
Category:general -- posted at: 8:00am PDT

Today on Data Skeptic, Lachlan Gunn joins us to discuss his recent paper Too Good to be True. This paper highlights a somewhat paradoxical / counterintuitive fact about how unanimity is unexpected in cases where perfect measurements cannot be taken. With large enough data, some amount of error is expected.

The "Too Good to be True" paper highlights three interesting examples which we discuss in the podcast. You can also watch a lecture from Lachlan on this topic via youtube here.

Direct download: too_good_to_be_true.mp3
Category:general -- posted at: 8:00am PDT

How well does your model explain your data? R-squared is a useful statistic for answering this question. In this episode we explore how it applies to the problem of valuing a house. Aspects like the number of bedrooms go a long way in explaining why different houses have different prices. There's some amount of variance that can be explained by a model, and some amount that cannot be directly measured. R-squared is the ratio of the explained variance to the total variance. It's not a measure of accuracy, it's a measure of the power of one's model.

Direct download: r-squared.mp3
Category:general -- posted at: 8:00am PDT

 
Direct download: think_again.mp3
Category:general -- posted at: 7:30am PDT

[MINI] Multiple Regression

This episode is a discussion of multiple regression: the use of observations that are a vector of values to predict a response variable. For this episode, we consider how features of a home such as the number of bedrooms, number of bathrooms, and square footage can predict the sale price.

Unlike a typical episode of Data Skeptic, these show notes are not just supporting material, but are actually featured in the episode.

The site Redfin gratiously allows users to download a CSV of results they are viewing. Unfortunately, they limit this extract to 500 listings, but you can still use it to try the same approach on your own using the download link shown in the figure below.

Direct download: multiple_regressions.mp3
Category:general -- posted at: 7:30am PDT

Samuel Mehr joins us this week to share his perspective on why people are musical, where music comes from, and why it works the way it does. We discuss a number of empirical studies related to music and musical cognition, and dispense a few myths about music along the way.

Some of Sam's work discussed in this episode include Music in the Home: New Evidence for an Intergenerational Link,Two randomized trials provide no consistent evidence for nonmusical cognitive benefits of brief preschool music enrichment, and Miscommunication of science: music cognition research in the popular press. Additional topics we discussed are also covered in a Harvard Gazette article featuring Sam titled Muting the Mozart effect.

You can follow Sam on twitter via @samuelmehr.

Direct download: samuel.mp3
Category:general -- posted at: 7:30am PDT

This episode reviews the concept of k-d trees: an efficient data structure for holding multidimensional objects. Kyle gives Linhda a dictionary and asks her to look up words as a way of introducing the concept of binary search. We actually spend most of the episode talking about binary search before getting into k-d trees, but this is a necessary prerequisite.

Direct download: kd_trees.mp3
Category:general -- posted at: 7:13am PDT

Algorithms are pervasive in our society and make thousands of automated decisions on our behalf every day. The possibility of digital discrimination is a very real threat, and it is very plausible for discrimination to occur accidentally (i.e. outside the intent of the system designers and programmers). Christian Sandvig joins us in this episode to talk about his work and the concept of auditing algorithms.

Christian Sandvig (@niftyc) has a PhD in communications from Stanford and is currently an Associate Professor of Communication Studies and Information at the University of Michigan. His research studies the predictable and unpredictable effects that algorithms have on culture. His work exploring the topic of auditing algorithms has framed the conversation of how and why we might want to have oversight on the way algorithms effect our lives. His writing appears in numerous publications including The Social Media Collective, The Huffington Post, and Wired.

One of his papers we discussed in depth on this episode was Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms, which is well worth a read.

Direct download: auditing_algorithms.mp3
Category:general -- posted at: 7:00am PDT

Today's episode begins by asking how many left handed employees we should expect to be at a company before anyone should claim left handedness discrimination. If not lefties, let's consider eye color, hair color, favorite ska band, most recent grocery store used, and any number of characteristics could be studied to look for deviations from the norm in a company.

When multiple comparisons are to be made simultaneous, one must account for this, and a common method for doing so is with the Bonferroni Correction. It is not, however, a sure fire procedure, and this episode wraps up with a bit of skepticism about it.

Direct download: bonferroni-correction2.mp3
Category:general -- posted at: 7:00am PDT

A recent paper in the journal of Judgment and Decision Making titled On the reception and detection of pseudo-profound bullshit explores empirical questions around a reader's ability to detect statements which may sound profound but are actually a collection of buzzwords that fail to contain adequate meaning or truth. These statements are definitively different from lies and nonesense, as we discuss in the episode.

This paper proposes the Bullshit Receptivity scale (BSR) and empirically demonstrates that it correlates with existing metrics like the Cognitive Reflection Test, building confidence that this can be a useful, repeatable, empirical measure of a person's ability to detect pseudo-profound statements as being different from genuinely profound statements. Additionally, the correlative results provide some insight into possible root causes for why individuals might find great profundity in these statements based on other beliefs or cognitive measures.

The paper's lead author Gordon Pennycook joins me to discuss this study's results.

If you'd like some examples of pseudo-profound bullshit, you can randomly generate some based on Deepak Chopra's twitter feed.

To read other work from Gordon, check out his Google Scholar page and find him on twitter via @GordonPennycook.

And just for fun, if you think you've dreamed up a Data Skeptic related pseudo-profound bullshit statement, tweet it with hashtag #pseudoprofound. If I see an especially clever or humorous one, I might want to send you a free Data Skeptic sticker.

 
Direct download: pseudo-profound-episode.mp3
Category:general -- posted at: 7:00am PDT

Today's mini episode discusses the widely known optimization algorithm gradient descent in the context of hiking in a foggy hillside.

Direct download: gradient_descent.mp3
Category:general -- posted at: 12:00am PDT

This episode is a discussion of data visualization and a proposed New Year's resolution for Data Skeptic listeners. Let's kill the word cloud.

Direct download: lets_kill_the_word_cloud.mp3
Category:general -- posted at: 12:00am PDT

Today's episode is a reading of Isaac Asimov's The Machine that Won the War. I can't think of a story that's more appropriate for Data Skeptic.

Direct download: 2015_Holiday_Special.mp3
Category:general -- posted at: 12:00am PDT

In this interview with Aaron Halfaker of the Wikimedia Foundation, we discuss his research and career related to the study of Wikipedia. In his paper The Rise and Decline of an open Collaboration Community, he highlights a trend in the declining rate of active editors on Wikipedia which began in 2007. I asked Aaron about a variety of possible hypotheses for the phenomenon, in particular, how automated quality control tools that revert edits automatically could play a role. This lead Aaron and his collaborators to develop Snuggle, an optimized interface to help Wikipedians better welcome new comers to the community.

We discuss the details of these topics as well as ORES, which provides revision scoring as a service to any software developer that wants to consume the output of their machine learning based scoring.

You can find Aaron on Twitter as @halfak.

Direct download: wikipedia-revision-scoring-as-a-service.mp3
Category:general -- posted at: 6:30am PDT

Today's topic is term frequency inverse document frequency, which is a statistic for estimating the importance of words and phrases in a set of documents.

Direct download: tf_-_idf.mp3
Category:general -- posted at: 8:45am PDT

Early astronomers could see several of the planets with the naked eye. The invention of the telescope allowed for further understanding of our solar system. The work of Isaac Newton allowed later scientists to accurately predict Neptune, which was later observationally confirmed exactly where predicted. It seemed only natural that a similar unknown body might explain anomalies in the orbit of Mercury, and thus began the search for the hypothesized planet Vulcan.

Thomas Levenson's book "The Hunt for Vulcan" is a narrative of the key scientific minds involved in the search and eventual refutation of an unobserved planet between Mercury and the sun. Thomas joins me in this episode to discuss his book and the fascinating story of the quest to find this planet.

During the discussion, we mention one of the contributions made by Urbain-Jean-Joseph Le Verrier which involved some complex calculations which enabled him to predict where to find the planet that would eventually be called Neptune. The calculus behind this work is difficult, and some of that work is demonstrated in a Jupyter notebook I recently discovered from Paulo Marques titled The-Body Problem.

Thomas Levenson is a professor at MIT and head of its science writing program. He is the author of several books, including Einstein in Berlin and Newton and the Counterfeiter: The Unknown Detective Career of the World’s Greatest Scientist. He has also made ten feature-length documentaries (including a two-hour Nova program on Einstein) for which he has won numerous awards. In his most recent book "The Hunt for Vulcan", explores the century spanning quest to explain the movement of the cosmos via theory and the role the hypothesized planet Vulcan played in the story.

Follow Thomas on twitter @tomlevenson and check out his blog athttps://inversesquare.wordpress.com/.

Pick up your copy of The Hunt for Vulcan at your local bookstore, preferred book buying place, or at the Penguin Random House site.

Direct download: the-hunt-for-vulcan.mp3
Category:general -- posted at: 12:00am PDT

Today's episode discusses the accuracy paradox. There are cases when one might prefer a less accurate model because it yields more predictive power or better captures the underlying causal factors describing the outcome variable you are interested in. This is especially relevant in machine learning when trying to predict rare events. We discuss how the accuracy paradox might apply if you were trying to predict the likelihood a person was a bird owner.

Direct download: the-accuracy-paradox.mp3
Category:general -- posted at: 12:00am PDT

... or should this have been called data science from a neuroscientist's perspective? Either way, I'm sure you'll enjoy this discussion with Laurie Skelly. Laurie earned a PhD in Integrative Neuroscience from the Department of Psychology at the University of Chicago. In her life as a social neuroscientist, using fMRI to study the neural processes behind empathy and psychopathy, she learned the ropes of zooming in and out between the macroscopic and the microscopic -- how millions of data points come together to tell us something meaningful about human nature. She's currently at Metis Data Science, an organization that helps people learn the skills of data science to transition in industry.

In this episode, we discuss fMRI technology, Laurie's research studying empathy and psychopathy, as well as the skills and tools used in common between neuroscientists and data scientists. For listeners interested in more on this subject, Laurie recommended the blogs Neuroskeptic, Neurocritic, and Neuroecology.

We conclude the episode with a mention of the upcoming Metis Data Science San Francisco cohort which Laurie will be teaching. If anyone is interested in applying to participate, they can do so here.

Direct download: neuroscience.mp3
Category:general -- posted at: 12:00am PDT

A discussion of the expected number of cars at a stoplight frames today's discussion of the bias variance tradeoff. The central ideal of this concept relates to model complexity. A very simple model will likely generalize well from training to testing data, but will have a very high variance since it's simplicity can prevent it from capturing the relationship between the covariates and the output. As a model grows more and more complex, it may capture more of the underlying data but the risk that it overfits the training data and therefore does not generalize (is biased) increases. The tradeoff between minimizing variance and minimizing bias is an ongoing challenge for data scientists, and an important discussion for skeptics around how much we should trust models.

Direct download: bias-variance-tradeoff.mp3
Category:general -- posted at: 12:00am PDT