Barbara started by introducing machine learning (ML), gave a brief overview of R and then discussed three examples; classifying hand written digits, estimating values in a socio-economic dataset and clustering crimes in Chicago. ML is statistics in steroids. ML uses data to find that pattern then uses that pattern (model) to predict results from similar data. Barbra uses the example of classifying film genres into either action or romance based on the number of kicks and kisses. Barbara described supervised and unsupervised. Unsupervised is the "wild, wild west" we can't train the model and it is much more difficult to understand how effective these are. Back to supervised learning, it's important to choose good predicting factors – in the movie example perhaps the title, actors, script may have been better predictors that the number of kicks and kisses. Then you must choose the algorithm and then tune it and finally make it useful and visible and get it into production - it's a hard job especially when data scientists and software developer seem to be different tribes.

Many problems on signal processing reduce to nonparametric function estimation. We propose a new methodology, piecewise convex fitting (PCF), and give a two-stage adaptive estimate. In the first stage, the number and location of the change points is estimated using strong smoothing. In the second stage, a constrained smoothing spline fit is performed with the smoothing level chosen to minimize the MSE. The imposed constraint is that a single change point occurs in a region about each empirical change point of the first-stage estimate. This constraint is equivalent to requiring that the third derivative of the second-stage estimate has a single sign in a small neighborhood about each first-stage change point. We sketch how PCF may be applied to signal recovery, instantaneous frequency estimation, surface reconstruction, image segmentation, spectral estimation and multivariate adaptive regression.

Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation. We propose a distributed optimization framework where the dataset is "encoded" to have an over-complete representation with built-in redundancy, and the straggling nodes in the system are dynamically left out of the computation at every iteration, whose loss is compensated by the embedded redundancy. We show that oblivious application of several popular optimization algorithms on encoded data, including gradient descent, L-BFGS, proximal gradient under data parallelism, and coordinate descent under model parallelism, converge to either approximate or exact solutions of the original problem when stragglers are treated as erasures. These convergence results are deterministic, i.e., they establish sample path convergence for arbitrary sequences of delay patterns or distributions on the nodes, and are independent of the tail behavior of the delay distribution. We demonstrate that equiangular tight frames have desirable properties as encoding matrices, and propose efficient mechanisms for encoding large-scale data. We implement the proposed technique on Amazon EC2 clusters, and demonstrate its performance over several learning problems, including matrix factorization, LASSO, ridge regression and logistic regression, and compare the proposed method with uncoded, asynchronous, and data replication strategies.

We propose a method for domain adaptation on graphs. Given sufficiently many observations of the label function on a source graph, we study the problem of transferring the label information from the source graph to a target graph for estimating the target label function. Our assumption about the relation between the two domains is that the frequency content of the label function, regarded as a graph signal, has similar characteristics over the source and the target graphs. We propose a method to learn a pair of coherent bases on the two graphs, such that the corresponding source and target graph basis vectors have similar spectral content, while "aligning" the two graphs at the same time so that the reconstructed source and target label functions have similar coefficients over the bases. Experiments on several types of data sets suggest that the proposed method compares quite favorably to reference domain adaptation methods. To the best of our knowledge, our treatment is the first to study the domain adaptation problem in a purely graph-based setting with no need for embedding the data in an ambient space. This feature is particularly convenient for many problems of interest concerning learning on graphs or networks.

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.

This paper investigates asymptotic behaviors of gradient descent algorithms (particularly accelerated gradient descent and stochastic gradient descent) in the context of stochastic optimization arose in statistics and machine learning where objective functions are estimated from available data. We show that these algorithms can be modeled by continuous-time ordinary or stochastic differential equations, and their asymptotic dynamic evolutions and distributions are governed by some linear ordinary or stochastic differential equations, as the data size goes to infinity. We illustrate that our study can provide a novel unified framework for a joint computational and statistical asymptotic analysis on dynamic behaviors of these algorithms with the time (or the number of iterations in the algorithms) and large sample behaviors of the statistical decision rules (like estimators and classifiers) that the algorithms are applied to compute, where the statistical decision rules are the limits of the random sequences generated from these iterative algorithms as the number of iterations goes to infinity.

Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage flexible volume preserving flows to obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to train efficient Markov chains to sample from a prescribed posterior distribution by iteratively improving the quality of both the model and the samples. A-NICE-MC provides the first framework to automatically design efficient domain-specific MCMC proposals. Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo.

Once we have formed our training data-set, which is represented as an M x N matrix where M is the number of data points and N is the number of features, we can now begin classifying. There are two important decisions that must be made before making classifications. One is the value of k that will be used; this can either be decided arbitrarily, or you can try cross-validation to find an optimal value. The next, and the most complex, is the distance metric that will be used. There are many different ways to compute distance, as it is a fairly ambiguous notion, and the proper metric to use is always going to be determined by the data-set and the classification task.

Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it a decision tree or xgboost, caret helps to find the optimal model in the shortest possible time. Caret nicely integrates all the activities associated with the model development in a streamlined workflow, for nearly every major ML algorithm available in R. Actually we will not just stop with the caret package but will also go a step ahead and see how to smartly ensemble predictions from multiple best models and possibly produce an even better prediction using caretEnsemble. Caret is short for Classification And REgression Training. With R having so many implementations of machine learning algorithms, spread across packages it may be challenging to keep track of which algorithm resides in which package. Sometimes the syntax and the way to implement the algorithm differ across packages combined with preprocessing and looking at the help page for the hyperparameters (parameters that define how the algorithm learns) can make building predictive models an involved task. Well, thanks to caret because no matter which package the algorithm resides, caret will remember that for you and may just prompt you to run install.package Later in this tutorial I will show how to see all the available ML algorithms supported by caret (it's a long list!) and what hyperparameters can be tuned.

Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of y, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest onestep-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. It was found that both the random forest and RF-PLS methods most adequately modeled the datasets that did not feature purely monotonic increases in property values, but that both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed. Preprint submitted to Arxiv March 14, 2018 1. Introduction Soft sensors for regression tasks have found wide utility in process engineering and process analytical chemistry [1, 2, 3]. A soft sensor is effectively a calibration used on time-series data. Here, we consider a soft sensor to be any algorithm that can be used to estimate a property value from several readily available but indirect measurements. The goal of implementing a soft sensor is typically to avoid the use of a physical sensor for variables that may require extensive time or work up to measure [3]. In the context of industrial chemical processes, these algorithms should meet several specifications.