Today, big and small companies around the world are racing to adopt the latest tools in artificial intelligence and machine learning. While data is often positioned as the blanket cure for every business malady, those who work in the field understand all too well that data science algorithms are never a one-size-fits-all solution. As the field rapidly evolves, there are a growing number of advanced algorithms available for businesses to deploy in their day-to-day operations. From tools based on deep neural networks, clustering algorithms to time-series analysis, these solutions can resolve a wide range of business problems. However, out of this mass of options, the biggest challenge for an organization may be as simple as sourcing the right data and asking the right questions.

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Disclaimer: I'll be talking mainly about logistic-regression and basic feed-forward neural networks, so its helpful to have programmed with those 2 models before reading this piece. OK -- before statisticians and ML folks come running after me after reading the title, I'm not talking about linear regression, for example. Yes, in linear regression, you can use the R-squared (or adjusted R-squared statistic) to talk about explained variance, and since linear regression only involves addition between independent variables (or predictors), they're pretty interpretable. If you were doing a linear regression to predict, say the price of a car Car_Price, based on the number of seats, mileage, maximum-speed, and battery life, your linear model could be –– say Car_Price c1*Seats c2*Mileage c3*Speed c4*Battery_Power –– the fact that variables are only added makes it pretty interpretable. But when it comes to more complex prediction models like Logistic Regression and neural networks, everything about the predictors (or called "features" in ML) becomes more confusing.

You must have heard about machine learning as it has become a buzzword. Machine learning is an innovative method of analyzing data that has the capability to automate analytical model building. It is a field of computer science and an important branch of artificial intelligence. Machine learning is based on the revolutionary idea that computer systems could learn from data, just like humans. As a result, they can identify patterns and make informed decisions without resorting to much human intervention. Machine learning is now a keyword in the world of technology.

Cluster-based information retrieval is one of the Information retrieval(IR) tools that organize, extract features and categorize the web documents according to their similarity. Unlike traditional approaches, cluster-based IR is fast in processing large datasets of document. To improve the quality of retrieved documents, increase the efficiency of IR and reduce irrelevant documents from user search. in this paper, we proposed a (K-means) - Hierarchical Parallel Genetic Algorithms Approach (HPGA) that combines the K-means clustering algorithm with hybrid PG of multi-deme and master/slave PG algorithms. K-means uses to cluster the population to k subpopulations then take most clusters relevant to the query to manipulate in a parallel way by the two levels of genetic parallelism, thus, irrelevant documents will not be included in subpopulations, as a way to improve the quality of results. Three common datasets (NLP, CISI, and CACM) are used to compute the recall, precision, and F-measure averages. Finally, we compared the precision values of three datasets with Genetic-IR and classic-IR. The proposed approach precision improvements with IR-GA were 45% in the CACM, 27% in the CISI, and 25% in the NLP. While, by comparing with Classic-IR, (k-means)-HPGA got 47% in CACM, 28% in CISI, and 34% in NLP.

Abstract--Bayesian networks are powerful statistical models to study the probabilistic relationships among set random variables with major applications in disease modeling and prediction. Here, we propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression, to model the impact of exogenous variables on the conditional dependencies of the network. We also propose an adaptive regularization method with an intuitive early stopping feature based on density based clustering for efficient learning of the structure and parameters of the proposed network. Using a dataset of patients with multiple chronic conditions extracted from electronic health records of the Department of Veterans Affairs we compare the performance of the proposed approach with some of the existing methods in the literature for both short-term (one-year ahead) and long-term (multi-year ahead) predictions. The proposed approach provides a sparse intuitive representation of the complex functional relationships between multiple chronic conditions. It also provides the capability of analyzing multiple disease trajectories over time given any combination of prior conditions.

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.

Finding meaningful clusters in drive-by-download malware data is a particularly difficult task. Malware data tends to contain overlapping clusters with wide variations of cardinality. This happens because there can be considerable similarity between malware samples (some are even said to belong to the same family), and these tend to appear in bursts. Clustering algorithms are usually applied to normalised data sets. However, the process of normalisation aims at setting features with different range values to have a similar contribution to the clustering. It does not favour more meaningful features over those that are less meaningful, an effect one should perhaps expect of the data pre-processing stage. In this paper we introduce a method to deal precisely with the problem above. This is an iterative data pre-processing method capable of aiding to increase the separation between clusters. It does so by calculating the within-cluster degree of relevance of each feature, and then it uses these as a data rescaling factor. By repeating this until convergence our malware data was separated in clear clusters, leading to a higher average silhouette width.

In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user's degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user's time since beginning treatment, thus addressing challenge three. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art. We demonstrate the promise of this approach and its ability to learn from even a small group of users in a live clinical trial.

Deep Reinforcement Learning (DRL) methods often rely on the meticulous tuning of hyperparameters to successfully resolve problems. One of the most influential parameters in optimization procedures based on stochastic gradient descent (SGD) is the learning rate. We investigate cyclical learning and propose a method for defining a general cyclical learning rate for various DRL problems. In this paper we present a method for cyclical learning applied to complex DRL problems. Our experiments show that, utilizing cyclical learning achieves similar or even better results than highly tuned fixed learning rates. This paper presents the first application of cyclical learning rates in DRL settings and is a step towards overcoming manual hyperparameter tuning.