Get your team access to Udemy's top 2,000 courses anytime, anywhere. This course has two parts. In part 1 Association rules (Market Basket Analysis) is explained. In Part 2, Linear Discriminant Analysis (LDA) is explained.

Feature selection is a part of Machine Learning that can be very difficult to grasp for someone not intimately familiar with statistics. It is the intent of this blog to demystify what feature selection is and the techniques that can be used to accomplish it. Making Predictions and Cluster Analysis are two technologies at the heart of Machine Learning and Statistics. These technologies are being used today to help analyze the "information overload" that characterizes big data. There are a wide variety of Clustering Techniques available today including: K-Means, Hierarchical, Spectral, Density, Mean Shift, and Biclustering.

A few years ago, Scott Fortmann-Roe wrote a great essay titled "Understanding the Bias-Variance Tradeoff." As data science morphs into an accepted profession with its own set of tools, procedures, workflows, etc., there often seems to be less of a focus on statistical processes in favor of the more exciting aspects (see here and here for a pair of example discussions). While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Of course you only have one model so talking about expected or average prediction values might seem a little strange. However, imagine you could repeat the whole model building process more than once: each time you gather new data and run a new analysis creating a new model.

Abstract--Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and classifier, there exists so called universal adversarial perturbations, a single perturbation that causes a misclassification when applied to any input. In this work, we introduce universal adversarial networks, a generative network that is capable of fooling a target classifier when it's generated output is added to a clean sample from a dataset. We show that this technique improves on known universal adversarial attacks. Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [22], [30], [41], robotics and automotive [28], [32], [40], security [2], [17], [38] and financial [13], [18], [36] applications. Recent research shows that machine learning models trained on entirely uncorrupted data, are still vulnerable to adversarial examples [7], [12], [23], [24], [35], [37]: samples that have been maliciously altered so as to be misclassified by a target model while appearing unaltered to the human eye.

Machine Learning models have been shown to be vulnerable to adversarial examples, ie. the manipulation of data by a attacker to defeat a defender's classifier at test time. We present a novel probabilistic definition of adversarial examples in perfect or limited knowledge setting using prior probability distributions on the defender's classifier. Using the asymptotic properties of the logistic regression, we derive a closed-form expression of the intensity of any adversarial perturbation, in order to achieve a given expected misclassification rate. This technique is relevant in a threat model of known model specifications and unknown training data. To our knowledge, this is the first method that allows an attacker to directly choose the probability of attack success. We evaluate our approach on two real-world datasets.

Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, the outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters. However, most existing studies handle them separately. In light of this, we consider the joint cluster analysis and outlier detection problem, and propose the Clustering with Outlier Removal (COR) algorithm. Generally speaking, the original space is transformed into the binary space via generating basic partitions in order to define clusters. Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed. With further analyses on the objective function, only partial of the problem can be handled by K-means optimization. To provide an integrated solution, an auxiliary binary matrix is nontrivally introduced so that COR completely and efficiently solves the challenging problem via a unified K-means- - with theoretical supports. Extensive experimental results on numerous data sets in various domains demonstrate the effectiveness and efficiency of COR significantly over the rivals including K-means- - and other state-of-the-art outlier detection methods in terms of cluster validity and outlier detection. Some key factors in COR are further analyzed for practical use. Finally, an application on flight trajectory is provided to demonstrate the effectiveness of COR in the real-world scenario.

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets.

At some fundamental level, no one understands machine learning. It isn't a matter of things being too complicated. Almost everything we do is fundamentally very simple. Unfortunately, an innate human handicap interferes with us understanding these simple things. Humans evolved to reason fluidly about two and three dimensions. With some effort, we may think in four dimensions. Machine learning often demands we work with thousands of dimensions – or tens of thousands, or millions! Even very simple things become hard to understand when you do them in very high numbers of dimensions. Reasoning directly about these high dimensional spaces is just short of hopeless.

I just read two articles that claim that Python is overtaking R for data science and machine learning. From user comments, I learned that R is still strong in certain tasks. I will survey what these tasks are. The first article by Vincent Granville from DSC uses proxy metrics (as opposed to asking the users). He uses statistics from Google Trends, Indeed job search terms, and Analytic Talent (DSC job database) to conclude that Python has overtaken R. One is led to ask if one group of users (say Python's) is a more active googler.

In recent years, the number of applications to the UTCS Ph.D. program has become too large to manage with a traditional review process. GRADE uses historical admissions data to predict how likely the committee is to admit each new applicant. It reports each prediction as a score similar to those used by human reviewers, and accompanies each by an explanation of what applicant features most influenced its prediction. GRADE makes the review process more efficient by enabling reviewers to spend most of their time on applicants near the decision boundary and by focusing their attention on parts of each applicant's file that matter the most. An evaluation over two seasons of Ph.D. admissions indicates that the system leads to dramatic time savings, reducing the total time spent on reviews by at least 74 percent.