I had the opportunity the other week to spend a couple of hours shooting the breeze with our lead data scientist Tijl Carpels. While we chatted over an overpriced macchiato on everything from Star Wars to crypto currency the talk very quickly settled on the use of supervised and unsupervised algorithms in AI. This all came about because we have launched a new bespoke AI product (ISAAC) and I really wanted to know more about how it worked and also why did we choose this path. So the theory is simple, but as many have experienced putting this into practice is fraught with dangers. The problem a lot of us have is that in the mid 90's many companies were sold'black box' platforms that promised a lot and delivered little.

Last year, tech giants started working together to combat the spread of terrorist content online. One of them is Google-owned YouTube, which began implementing stricter measures in June in an effort to get rid of extremist videos that tend to pop up on the platform. According to the video streaming website, its flagging technology is now good enough that over 83 percent of the terrorist-related videos it removed over the past month didn't stay online long enough to get a single flag from a human user. YouTube upgraded the technology by feeding it a huge volume of new training examples. The company tasked the people working on the feature to review over a million videos and find the right ones.

Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called thesupervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way.

In supervised machine learning, models are trained on a subset of data aka training data. The goal is to compute the target of each training example from the training data. Now, overfitting happens when model learns signal as well as noise in the training data and wouldn't perform well on new data on which model wasn't trained on. In the example below, you can see underfitting in first few steps and overfitting in last few. Now, there are few ways you can avoid overfitting your model on training data like cross-validation sampling, reducing number of features, pruning, regularization etc. Regularization basically adds the penalty as model complexity increases.

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. In part two, you will learn about Unsupervised Learning.

We investigate a family of regression problems in a semi-supervised setting. The task is to assign real-valued labels to a set of $n$ sample points, provided a small training subset of $N$ labeled points. A goal of semi-supervised learning is to take advantage of the (geometric) structure provided by the large number of unlabeled data when assigning labels. We consider random geometric graphs, with connection radius $\epsilon(n)$, to represent the geometry of the data set. Functionals which model the task reward the regularity of the estimator function and impose or reward the agreement with the training data. Here we consider the discrete $p$-Laplacian regularization. We investigate asymptotic behavior when the number of unlabeled points increases, while the number of training points remains fixed. We uncover a delicate interplay between the regularizing nature of the functionals considered and the nonlocality inherent to the graph constructions. We rigorously obtain almost optimal ranges on the scaling of $\epsilon(n)$ for the asymptotic consistency to hold. We prove that the minimizers of the discrete functionals in random setting converge uniformly to the desired continuum limit. Furthermore we discover that for the standard model used there is a restrictive upper bound on how quickly $\epsilon(n)$ must converge to zero as $n \to \infty$. We introduce a new model which is as simple as the original model, but overcomes this restriction.

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such approaches have only been proposed for linear models. In this work, we introduce semi-supervised parameter learning for Sum-Product Networks (SPNs). SPNs are deep probabilistic models admitting inference in linear time in number of network edges. Our approach has several advantages, as it (1) allows generative and discriminative semi-supervised learning, (2) guarantees that adding unlabelled data can increase, but not degrade, the performance (safe), and (3) is computationally efficient and does not enforce restrictive assumptions on the data distribution. We show on a variety of data sets that safe semi-supervised learning with SPNs is competitive compared to state-of-the-art and can lead to a better generative and discriminative objective value than a purely supervised approach.

Customer Experience (CX) is a top priority focal point in data-driven digital business transformation. Customer-centricity is not new, of course. However, in the modern digital business context, the conversation around customer-centricity focuses on steps to measure CX, to optimize CX, and to apply design thinking around CX across the full customer journey. This focus on experience management (measurement, optimization, and design thinking) has broader application in UX (User Experience) and DX (Digital Experience). In addition, we see experience journey management being discussed and applied in other domains, such as Healthcare (Patient Experience) and Human Resources and Human Capital Management (EX: Employee Experience).

Eric Sondheimer has been covering high school sports for the Los Angeles Times since 1997 and in Southern California since 1976. Get his latest from the field and follow all our prep sports coverage and analysis here. Soccer: Adidas West Coast Showcase set for Dec. 7-9 Bell Gardens, Downey, Paramount and Warren will be the sites for one of the top boys' soccer tournaments set for Dec. 7-9. The Adidas West Coast Showcase will bring together top teams from around Southern California. The West Division has Channel Islands, Clovis, Downey, Granada Hills, Long Beach Jordan, Moorpark, Palos Verdes and Warren.