At Geneia, a health care technology and consulting company, they use big data along with machine learning to help health care organizations deliver better patient care at a lower cost. "Geneia brings data in from a lot of different sources," said Lavoie. We've built Theon, a unified platform to integrate the data and allow us to apply machine learning techniques." Using machine learning, Geneia can match and determine missing values, as well as perform principal component analysis and look at patterns in the data – clusters that help them see trends and causality. "Machine learning allows us to see patterns in the data that we couldn't see before.

From May to July 2016, over one thousand Kagglers competed in Facebook's fifth recruitment competition: Predicting Check-Ins. In this challenge, Kagglers were required to predict the most probable check-in locations occurring in artificial time and space. As the first place winner, Tom Van de Wiele, notes in this winner's interview, the uniquely designed test dataset contained about one trillion place-observation combinations, posing a huge difficulty to competitors. Tom describes how he quickly rocketed from his first getting started competition on Kaggle to first place in Facebook V through his remarkable insight into data consisting only of x,y coordinates, time, and accuracy using k-nearest neighbors and XGBoost. I have completed two Master programs at two different Belgian universities (Leuven and Ghent), one in Computer Science (2010) and one in Statistics (2016).

Wine tasting is a fine art which enables classification of Wine. When it comes to classification of wine, the practice is quite varied based on region of origin and time. It is one of the most tasteful traditions which is also protected by law of its own in certain regions. The classification varies based on vintage, sweetness, appellation, vinification styles, varietal or blend. Is it possible to teach something about the classification of different variety of wines to Neural Networks? Well, I intend to do exactly that and get hands on with code as well.

These are lecture notes that are based on the lectures from a class I taught on the topic of Spectral Graph Methods at UC Berkeley during the Spring 2015 semester.

Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Recent work has shown that using a low-rank factorization of this kernel provides remarkable scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. In this paper we present a low-rank DPP mixture model that allows us to represent the latent structure present in observed subsets as a mixture of a number of component low-rank DPPs, where each component DPP is responsible for representing a portion of the observed data. The mixture model allows us to effectively address the capacity constraints of the low-rank DPP model. We present an efficient and scalable Markov Chain Monte Carlo (MCMC) learning algorithm for our model that uses Gibbs sampling and stochastic gradient Hamiltonian Monte Carlo (SGHMC). Using an evaluation on several real-world product recommendation datasets, we show that our low-rank DPP mixture model provides substantially better predictive performance than is possible with a single low-rank or full-rank DPP, and significantly better performance than several other competing recommendation methods in many cases.

Research into disease-related health variables typically involve choosing health variables and conditions, and using statistical methods to study the strength of association of the variables with the condition [9]. These are then used to confirm known or suspected relationships between the behavioural/health factors or disease conditions. There may be information about health status that may be gleaned by considering different aspects of an individual's data, and investigating possible relationships between the variables. Representations that capture these relationships can be useful in predicting presence or risk level of medical conditions. The National Health and Nutrition Examination Survey (NHANES) dataset provides data on health measurements, taken from survey participants, comprising different categories including demographics, laboratory tests and physical measurements.

Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We present a supervised discriminative High-Order Parametric Embedding (HOPE) approach to data visualization and compression. Compared to deep embedding models with complicated deep architectures, HOPE generates more effective high-order feature mapping through an embarrassingly simple shallow model. Furthermore, two approaches to generating a small number of exemplars conveying high-order interactions to represent large-scale data sets are proposed. These exemplars in combination with the feature mapping learned by HOPE effectively capture essential data variations. Moreover, through HOPE, these exemplars are employed to increase the computational efficiency of kNN classification for fast information retrieval by thousands of times. For classification in two-dimensional embedding space on MNIST and USPS datasets, our shallow method HOPE with simple Sigmoid transformations significantly outperforms state-of-the-art supervised deep embedding models based on deep neural networks, and even achieved historically low test error rate of 0.65% in two-dimensional space on MNIST, which demonstrates the representational efficiency and power of supervised shallow models with high-order feature interactions.

Abstract--We consider N -way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained T ensor Decomposition (SCTD) is based upon the CANDECOMP/PARAF AC (CP) decomposition which produces r -rank approximations of data tensors via outer products of vectors in each dimension of the data. By constraining the vector in the temporal dimension to known analytic forms which are selected from a large set of candidate functions, more readily interpretable decompositions are achieved and analytic time dependencies discovered. The SCTD method circumvents traditional flattening techniques where an N -way array is reshaped into a matrix in order to perform a singular value decomposition. A clear advantage of the SCTD algorithm is its ability to extract transient and intermittent phenomena which is often difficult for SVD-based methods. We motivate the SCTD method using several intuitively appealing results before applying it on a number of high-dimensional, real-world data sets in order to illustrate the efficiency of the algorithm in extracting interpretable spatiotemporal modes. With the rise of data-driven discovery methods, the decomposition proposed provides a viable technique for analyzing multitudes of data in a more comprehensible fashion. A TRIX decompositions are critically enabling algorithms for scientific computing and data analysis applications across every field of the engineering, social, biological, and physical sciences. Of particular importance is the singular value decomposition (SVD), which provides a principled method for dimensionality reduction and computation of interpretable subspaces within which the data reside. So widespread is the usage of the algorithm, and minor modifications thereof, that it has generated a myriad of names across various communities, including Principal Component Analysis (PCA) [1], the Karhunen-Lo eve (KL) decomposition, Hotelling transform [2], [3], Empirical Orthogonal Functions (EOFs) [4] and Proper Orthogonal Decomposition (POD) [5], [6]. However, in order to use the SVD, data, which generally may be of N distinct dimensions, must be flattened into a matrix form, potentially compromising the statistical accuracy of the subspaces computed. B. Lusch and J. N. Kutz are with the Department of Applied Mathematics, University of Washington, Seattle, W A 98195-3925 USA email: herwaldt@uw.edu, E. Chi is with the Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203 USA email: eric chi@ncsu.edu. It is often the case that one of the dimensions considered in the tensor is the time variable.

Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibit large degrees of heterogeneity, and furthermore there is not always a single readily available biomarker to quantify disease severity. Even when such a clinical variable exists, there are often additional related biomarkers routinely measured for patients that may better inform the predictions of their future disease state. To this end, we propose a novel probabilistic generative model for multivariate longitudinal data that captures dependencies between multivariate trajectories. We use a Gaussian process based regression model for each individual trajectory, and build off ideas from latent class models to induce dependence between their mean functions. We fit our method using a scalable variational inference algorithm to a large dataset of longitudinal electronic patient health records, and find that it improves dynamic predictions compared to a recent state of the art method. Our local accountable care organization then uses the model predictions during chart reviews of high risk patients with chronic kidney disease.

In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.