I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.

What is artificial intelligence, machine learning, and deep learning mean in general? When is a rule-based approach the right solution and when do you need machine learning? What does machine learning mean for time-series data? What is the difference between supervised and unsupervised learning in this area? Thanks to Devoxx for giving us permission to post this talk.

The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.

If you've walked around any security conferences recently, you'll have heard dozens of vendors talking about artificial intelligence (AI) and machine learning. But what do they actually do? Are they really going to usher in a grand age of cybersecurity? And are security analysts about to be collectively out of a job? Recently, Recorded Future co-hosted a webinar with SANS Institute with the goal of helping security conscious organizations understand how machine learning can help them process an almost infinite number of inputs into a small number of actionable outputs.

Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datasets with a number of large-domain categorical features are a difficult challenge for any classifier. Most off-the-shelf solutions cannot cope with this problem. In this work we introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. Furthermore, it adopts several novel techniques to reach high scalability without sacrificing quality, among which a preventive pruning of classification rules in the extraction phase based on Gini impurity. We ran experiments on Apache Spark, on a real large-scale dataset with more than 4 billion records and 800 million distinct categories. The results showed that DAC improves on a state-of-the-art solution in both prediction quality and execution time. Since the generated model is human-readable, it can not only classify new records, but also allow understanding both the logic behind the prediction and the properties of the model, becoming a useful aid for decision makers.

With online criminals getting highly skilled at attacking enterprises every day, it's getting difficult for businesses to tell the difference between legitimate and fraudulent activity. Fraud detection has always been a cat and mouse game. With improvements in techniques to detect and prevent frauds, fraudsters keep changing their attack patterns to unearth new holes and vulnerabilities – which the detection solutions then shore up. However, the nature of this game is changing. The days where enterprises were able to keep fraudsters at bay by using static detection rules are long gone.

You've probably been to a supermarket that printed coupons for you at checkout. Or listened to a playlist that your streaming service generated for you. Or gone shopping online and seen a list of products labeled "you might be interested in…." that did indeed contain some stuff that you were interested in. Recommendation engines take data about you, similar consumers, and available products, and use that to figure out what you might be interested in and therefore deliver those coupons, playlists, and suggestions. Recommendation engines can be extremely complex.

The concept of machine learning, which is a subset of artificial intelligence, has been around for some time. Ali Ghodsi, an adjunct professor at UC Berkeley, describes it as "an advanced statistical technique to make predictions on a massive amount of data." Ghodsi has been influential in areas of Big Data, distributed systems, and in machine learning projects including Apache Spark, Apache Hadoop, and Apache Mesos. Here, he shares insight on these projects, various use-cases, and the future of machine learning. There are some commonalities among these three projects that have been influenced by Ghodsi's research.

The effective use and adoption of machine learning requires algorithms that are not only accurate but also understandable. To address this need, BigML now includes functionality that allows for prediction explanation, model-independent explanations of classification, and regression predictions. In this post, we will summarize what it means for a prediction to be explainable, why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, machine learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are and how they are applied to new data is generally referred to as the interpretability or explanation of the model.

Technology's tentacles have encroached every aspect of our lives. Sitting in the comfort of your home you can tune in to live discussions and gain new understanding about technologies that are reshaping our world view. NDTV Tech Conclave 2018 saw a congregation of leading minds in the technology, mobile, and digital industries. The conclave aimed to showcase and create opportunities by bringing together many of the top entrepreneurs, investors, enterprise leaders, academics, and policymakers from around the world. The moderator of this session outlined two diametrically opposite views of AI and threw it open to the panelists.