The first 2 of them may appear similar at first glance, but actually they make totally different statements. In the first one (Data Science Venn Diagram 2.0) for example, mathematics and statistics are a real subset of Data Science, whereas in the second one Data Science is a real subset of Mathematics, which is a completely different statement. Another question from the first one is, whether Data Science really covers all of computer science, mathematics and statistics etc. This does not sound reasonable. The third diagram tries to explain what exactly is meant by mathematics and statistics.

Once awhile ago, in a usual busy work morning, I was pulled into meeting which my colleague said I absolutely need to participate because someone was going to present a success story of Machine Learning. Being an analytics evangelist in the company, I was of course excited about it! At the end of the presentation, I raised my hand to ask the presenter a question..."Can you help me understand how was Machine Learning applied in this use case?". The presenter replied..."I passed through these data into a Regression Model to produce these prediction results on my laptop." Unfortunately, nowadays, the phrase "machine learning" has become an overused buzzword just like "big data".

Since presidential campaigns have incorporated social media into their strategic messaging, it has become more challenging for journalists to cover the election in depth, because of the large amount of data generated by candidates and the public every day. Journalists tend to focus on single quotes or tweets rather than providing analysis and reporting on the aggregate of messages on social media. But single tweets may not give people a full appreciation for the style of campaigning or the substance of the rest of the tweets. In order to get a sense of what the candidates and public are actually saying and how candidates communicate over time, we have taken a computational approach to predict categories of candidate-produced tweets and posts (as described in a blog post introducing the Illuminating 2016 project). We have been working on a system that automatically classifies each message into a category based on what the message is trying to do: urge people to act, change their opinions through persuasion, inform them about some activity or event, honoring or mourning people or holidays, or on Twitter having a conversation with members of the public.

Object queries are essential in information seeking and decision making in vast areas of applications. However, a query may involve complex conditions on objects and sets, which can be arbitrarily nested and aliased. The objects and sets involved as well as the demand---the given parameter values of interest---can change arbitrarily. How to implement object queries efficiently under all possible updates, and furthermore to provide complexity guarantees? This paper describes an automatic method. The method allows powerful queries to be written completely declaratively. It transforms demand as well as all objects and sets into relations. Most importantly, it defines invariants for not only the query results, but also all auxiliary values about the objects and sets involved, including those for propagating demand, and incrementally maintains all of them. Implementation and experiments with problems from a variety of application areas, including distributed algorithms and probabilistic queries, confirm the analyzed complexities, trade-offs, and significant improvements over prior work.

Spectral embedding uses eigenfunctions of the discrete Laplacian on a weighted graph to obtain coordinates for an embedding of an abstract data set into Euclidean space. We propose a new pre-processing step of first using the eigenfunctions to simulate a low-frequency wave moving over the data and using both position as well as change in time of the wave to obtain a refined metric to which classical methods of dimensionality reduction can then applied. This is motivated by the behavior of waves, symmetries of the wave equation and the hunting technique of bats. It is shown to be effective in practice and also works for other partial differential equations -- the method yields improved results even for the classical heat equation.

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results. As the ground truth is known here, we also apply different cluster quality metrics to judge the goodness of fit of the cluster labels to the ground truth.

I see a lot of implementations use GIS or IIS to train the maximum entropy model. Can we use gradient desent method? If we can use it, why most tutorial directly tell GIS or IIS methos, but do not show the simple gradient desent method to train maximum entropy model? As we know, softmax regression is equivalent to the maxent model, but I never heard GIS or IIS in softmax. Is there a toy code use simple gradient desent method to train maxent model?

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.