Udemy Course Data Science & Machine Learning For Non Technical Executives NED Data Science & Machine Learning For Non Technical Executives free download also includes 8 hours on-demand video, 3 articles, 34 downloadable resources, Full lifetime access by Ankit Mistry Basic idea bout Machine learning technology Different ML algorithm like Regression, Classification & Clustering KNN and Logistic Regression algorithm Linear and Multiple Regression K means Clustering algorithm Overview about Deep Learning, Computer Vision Field Description Welcome to course on Data Science & Machine Learning For Non Technical Executives. Disclaimer: This is not python based machine learning course. I would highly suggest you not to enroll in this course if you are interested in implementation part of machine learning algorithm. There are many course on Udemy which teach machine learning with R/Python. I have designed this course for absolute beginner and non technical people who just want to start diving into machine learning world.

Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as motion segmentation, face and digit clustering. In face clustering, the subspaces are linear and subspace clustering methods can be applied directly. In motion segmentation, the subspaces are affine and an additional affine constraint on the coefficients is often enforced. However, since affine subspaces can always be embedded into linear subspaces of one extra dimension, it is unclear if the affine constraint is really necessary. This paper shows, both theoretically and empirically, that when the dimension of the ambient space is high relative to the sum of the dimensions of the affine subspaces, the affine constraint has a negligible effect on clustering performance. Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data. Underlying our analysis is the notion of affinely independent subspaces, which not only provides geometrically interpretable correctness conditions, but also clarifies the relationships between existing results for affine subspace clustering.

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion -- as is typical in real world situations -- the joint model produced by FL suffers in terms of test set accuracy and/or communication costs compared to training on iid data. We show that learning a single joint model is often not optimal in the presence of certain types of non-iid data. In this work we present a modification to FL by introducing a hierarchical clustering step (FL+HC) to separate clusters of clients by the similarity of their local updates to the global joint model. Once separated, the clusters are trained independently and in parallel on specialised models. We present a robust empirical analysis of the hyperparameters for FL+HC for several iid and non-iid settings. We show how FL+HC allows model training to converge in fewer communication rounds (significantly so under some non-iid settings) compared to FL without clustering. Additionally, FL+HC allows for a greater percentage of clients to reach a target accuracy compared to standard FL. Finally we make suggestions for good default hyperparameters to promote superior performing specialised models without modifying the the underlying federated learning communication protocol.

These methods, known as Mahalanobis metric learning approaches, have been analyzed Classical linear metric learning methods have recently theoretically, are scalable, and usually involve convex optimization been extended along two distinct lines: problems that can be solved globally (Kulis, 2013; deep metric learning methods for learning embeddings Bellet et al., 2015). of the data using neural networks, and Classical metric learning methods have been extended along Bregman divergence learning approaches for extending various axes; two important directions are deep metric learning learning Euclidean distances to more general and Bregman divergence learning. Deep metric learning divergence measures such as divergences over approaches replace the linear mapping learned in Mahalanobis distributions. In this paper, we introduce deep metric learning methods with more general mappings Bregman divergences, which are based on learning that are learned via neural networks (Hoffer & Ailon, and parameterizing functional Bregman divergences 2015; Chopra et al., 2005). On the other hand, Bregman using neural networks, and which unify divergence methods replace the squared Euclidean distance and extend these existing lines of work. We show with arbitrary Bregman divergences (Bregman, 1967), and in particular how deep metric learning formulations, learn the underlying generating function of the Bregman kernel metric learning, Mahalanobis metric divergence via piecewise linear approximators (Siahkamari learning, and moment-matching functions for et al., 2019) or convex combinations of existing basis functions comparing distributions arise as special cases of (Wu et al., 2009).

Feature learning in the presence of a mixed type of variables, numerical and categorical types, is an important issue for related modeling problems. For simple neighborhood queries under mixed data space, standard practice is to consider numerical and categorical variables separately and combining them based on some suitable distance functions. Alternatives, such as Kernel learning or Principal Component do not explicitly consider the inter-dependence structure among the mixed type of variables. In this work, we propose a novel strategy to explicitly model the probabilistic dependence structure among the mixed type of variables by an undirected graph. Spectral decomposition of the graph Laplacian provides the desired feature transformation. The Eigen spectrum of the transformed feature space shows increased separability and more prominent clusterability among the observations. The main novelty of our paper lies in capturing interactions of the mixed feature type in an unsupervised framework using a graphical model. We numerically validate the implications of the feature learning strategy

For many internal auditors, artificial intelligence (AI) may seem like a daunting topic to tackle -- but that shouldn't stop them from considering how they can apply it to their work. Tools and techniques exist that can provide auditors with powerful, straightforward techniques to enhance their work. With an increased focus and urgency around the use of data to support internal audit activities, the time for next-generation pursuits, such as use of AI, is now. Following up on a previous blog post discussing the basics of AI for auditors, here we offer our thoughts on how internal audit organizations can get started with AI methods, such as machine learning (ML), to increase efficiency and coverage, better assign resources to areas that matter most, deliver more insight and even help identify leading indicators of risk. We also offer a specific example of ML applied to internal audit. Machine Learning Doesn't Have to Be Complex ML is an application of AI in which the system itself is designed with the ability to learn and improve from experience.

State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such representation to be sparse, sparse subspace clustering is guaranteed to produce a subspace-preserving data affinity where two points are connected only if they are from the same subspace. On the other hand, however, data points from the same subspace may not be well-connected, leading to the issue of over-segmentation. We introduce dropout to address the issue of over-segmentation, which is based on randomly dropping out data points in self-expressive model. In particular, we show that dropout is equivalent to adding a squared $\ell_2$ norm regularization on the representation coefficients, therefore induces denser solutions. Then, we reformulate the optimization problem as a consensus problem over a set of small-scale subproblems. This leads to a scalable and flexible sparse subspace clustering approach, termed Stochastic Sparse Subspace Clustering, which can effectively handle large scale datasets. Extensive experiments on synthetic data and real world datasets validate the efficiency and effectiveness of our proposal.

This paper proposes a simple method to extract from a set of multiple related time series a compressed representation for each time series based on statistics for the entire set of all time series. This is achieved by a hierarchical algorithm that first generates an alphabet of shapelets based on the segmentation of centroids for clustered data, before labels of these shapelets are assigned to the segmentation of each single time series via nearest neighbor search using unconstrained dynamic time warping as distance measure to deal with non-uniform time series lenghts. Thereby, a sequence of labels is assigned for each time series. Completion of the last label sequence permits prediction of individual time series. Proposed method is evaluated on two global COVID-19 datasets, first, for the number of daily net cases (daily new infections minus daily recoveries), and, second, for the number of daily deaths attributed to COVID-19 as of April 27, 2020. The first dataset involves 249 time series for different countries, each of length 96. The second dataset involves 264 time series, each of length 96. Based on detected anomalies in available data a decentralized exit strategy from lockdowns is advocated.

This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density-based) and three types of distance measures (Euclidean, dynamic time warping, and shape-based). We lay out six restrictions with special attention to making the benchmark as unbiased as possible. A phased evaluation approach was then designed for summarizing dataset-level assessment metrics and discussing the results. The benchmark study presented can be a useful reference for the research community on its own; and the dataset-level assessment metrics reported may be used for designing evaluation frameworks to answer different research questions.

A computational paradigm based on neuroscientific concepts is proposed and shown to be capable of online unsupervised clustering. Because it is an online method, it is readily amenable to streaming realtime applications and is capable of dynamically adjusting to macro-level input changes. All operations, both training and inference, are localized and efficient. The paradigm is implemented as a cognitive column that incorporates five key elements: 1) temporal coding, 2) an excitatory neuron model for inference, 3) winner-take-all inhibition, 4) a column architecture that combines excitation and inhibition, 5) localized training via spike timing de-pendent plasticity (STDP). These elements are described and discussed, and a prototype column is given. The prototype column is simulated with a semi-synthetic benchmark and is shown to have performance characteristics on par with classic k-means. Simulations reveal the inner operation and capabilities of the column with emphasis on excitatory neuron response functions and STDP implementations.