In a mixed generalized linear model, the objective is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular class of estimators which output the top two eigenvectors of a suitable data-dependent matrix. However, despite the wide applicability, their design is still obtained via heuristic considerations, and the number of samples $n$ needed to guarantee recovery is super-linear in the signal dimension $d$. In this paper, we develop exact asymptotics on spectral methods in the challenging proportional regime in which $n, d$ grow large and their ratio converges to a finite constant. By doing so, we are able to optimize the design of the spectral method, and combine it with a simple linear estimator, in order to minimize the estimation error. Our characterization exploits a mix of tools from random matrices, free probability and the theory of approximate message passing algorithms. Numerical simulations for mixed linear regression and phase retrieval display the advantage enabled by our analysis over existing designs of spectral methods.

Datacenter operators ensure fair and regular server maintenance by using automated processes to schedule maintenance jobs to complete within a strict time budget. Automating this scheduling problem is challenging because maintenance job duration varies based on both job type and hardware. While it is tempting to use prior machine learning techniques for predicting job duration, we find that the structure of the maintenance job scheduling problem creates a unique challenge. In particular, we show that prior machine learning methods that produce the lowest error predictions do not produce the best scheduling outcomes due to asymmetric costs. Specifically, underpredicting maintenance job duration has results in more servers being taken offline and longer server downtime than overpredicting maintenance job duration. The system cost of underprediction is much larger than that of overprediction. We present Acela, a machine learning system for predicting maintenance job duration, which uses quantile regression to bias duration predictions toward overprediction. We integrate Acela into a maintenance job scheduler and evaluate it on datasets from large-scale, production datacenters. Compared to machine learning based predictors from prior work, Acela reduces the number of servers that are taken offline by 1.87-4.28X, and reduces the server offline time by 1.40-2.80X.

There are many low-quality free courses and YouTube courses that provide no help in building strong machine learning fundamentals. You will end up even more confused and quit pursuing the career. I am a big advocate of paid courses, but you can also learn a lot from interactive free courses by Udacty, Coursera, and FastAI. These courses cover fundamentals and introduce you to supervised, unsupervised, and deep learning algorithms. You will be introduced to machine learning applications, examples, and building your first linear and logistic regression model on Jupyter Notebook.

When applying for a programming or data science job, machine learning certifications and certificates have the potential to help you stand out from the crowded pool of candidates. Whether you've just completed a course of study or passed an exam offered by a respected institution, obtaining a certificate or certification is a real accomplishment that indicates your knowledge, experience, and expertise in the field of machine learning. But, what certificates and certifications are right for you? In this article, you'll learn more about the difference between certificates and certifications and explore five of the most popular ones for machine learning available today. Though they are often confused, certificates and certifications are not the same.

Model explanation is an essential task in supervised machine learning. Explaining how a model can represent the information is crucial to understanding the dynamics that rule our data. Let's see some models that are easy to interpret. Data Scientists have the role to extract information from raw data. They aren't engineers, nor they are software developers.

Regression is often associated with the task of curve fitting -- given data samples for pairs of random variables (X, Y), find a function y = F (x) that captures the relationship between X and Y as well as possible. As the underlying population for the (X, Y) pairs becomes increasingly complex, much efforts have been devoted to learning more complex models for the (regression) function F; see [20, 49, 15] for some recent examples. In many data domains, however, due to the heterogeneity of the behavior of the response variable Y with respect to covariate X, no single function F can fit the data pairs well, no matter how complex F is. Many authors noticed this challenge and adopted a mixture modeling framework into the regression problem, starting with some earlier work of [51, 6, 14]. To capture the uncertain and highly heterogeneous behavior of response variable Y given covariate X, one needs more than one single regression model. Suppose that there are k different regression behaviors, one can represent the conditional distribution of Y given X by a mixture of k conditional density functions associated with k underlying (latent) subpopulations. One can draw from the existing modeling tools of conditional densities such as generalized linear models [39], or more complex components [28, 63, 22] to increase the model fitness for the regression task. Recently, mixture of regression models (alternatively, regression mixture models) have found their applications in a vast range of domains, including risk estimation [2], education [7], medicine [34, 43, 56] and transportation analysis [46, 47, 64]. Making inferences in mixture of regression models can be done in a classical frequentist framework (e.g., maximum conditional likelihood estimation [6]), or a Bayesian framework [27].

In this work, we study the numerical solution of inverse eigenvalue problems from a machine learning perspective. Two different problems are considered: the inverse Strum-Liouville eigenvalue problem for symmetric potentials and the inverse transmission eigenvalue problem for spherically symmetric refractive indices. Firstly, we solve the corresponding direct problems to produce the required eigenvalues datasets in order to train the machine learning algorithms. Next, we consider several examples of inverse problems and compare the performance of each model to predict the unknown potentials and refractive indices respectively, from a given small set of the lowest eigenvalues. The supervised regression models we use are k-Nearest Neighbours, Random Forests and Multi-Layer Perceptron. Our experiments show that these machine learning methods, under appropriate tuning on their parameters, can numerically solve the examined inverse eigenvalue problems.

The primary aim of this research was to find a model that best predicts which fallen angel bonds would either potentially rise up back to investment grade bonds and which ones would fall into bankruptcy. To implement the solution, we thought that the ideal method would be to create an optimal machine learning model that could predict bankruptcies. Among the many machine learning models out there we decided to pick four classification methods: logistic regression, KNN, SVM, and NN. We also utilized an automated methods of Google Cloud's machine learning. The results of our model comparisons showed that the models did not predict bankruptcies very well on the original data set with the exception of Google Cloud's machine learning having a high precision score. However, our over-sampled and feature selection data set did perform very well. This could likely be due to the model being over-fitted to match the narrative of the over-sampled data (as in, it does not accurately predict data outside of this data set quite well). Therefore, we were not able to create a model that we are confident that would predict bankruptcies. However, we were able to find value out of this project in two key ways. The first is that Google Cloud's machine learning model in every metric and in every data set either outperformed or performed on par with the other models. The second is that we found that utilizing feature selection did not reduce predictive power that much. This means that we can reduce the amount of data to collect for future experimentation regarding predicting bankruptcies.

Sometimes as engineers and scientists, we think of data only as bytes on RAM, matrices in GPUs, and numeric features that go into our predictive black-box. We forget they represent changes in some real-world patterns. For example, when real world events and trends arise, we tend to defer to Google first to acquire related information (i.e where to go for a hike, what does term X mean) -- which makes Google Search Trends a very good source of data for interpreting and understanding what is going on live around us. This is why we decided to study a complex interplay between Google Search trends using it to predict other temporal data, and see if perhaps it could be used as features for a temporal machine learning model, and any insights we can draw from it. In this project, we looked at how Google Trends data could be used as features for times series models or regression models.

Six machine-learning prediction models (logistic regression with and without splines, lasso regression, random forest, gradient-boosting machines [ …