Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework for the extraction of features that can be used for abstraction with supervised learning methods that is based on the XES IEEE standard for event logs. This framework can automatically abstract sensor-level events to their interpretation at the human activity level, after training it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction are more precise indeed.

Machine learning is well-suited for problem domains typically found in the enterprise, like making predictions with supervised learning methods (e.g. Deep learning is an area of machine learning that has achieved significant progress in certain application areas that include pattern recognition, image classification, natural language processing (NLP), autonomous driving, and so on. Machine learning techniques like random forests and gradient boosting often perform better in the enterprise problem space than deep learning.

Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.

This work proposes a novel method for semi-supervised learning from partially labeled massive network-structured datasets, i.e., big data over networks. We model the underlying hypothesis, which relates data points to labels, as a graph signal, defined over some graph (network) structure intrinsic to the dataset. Following the key principle of supervised learning, i.e., "similar inputs yield similar outputs", we require the graph signals induced by labels to have small total variation. Accordingly, we formulate the problem of learning the labels of data points as a non-smooth convex optimization problem which amounts to balancing between the empirical loss, i.e., the discrepancy with some partially available label information, and the smoothness quantified by the total variation of the learned graph signal. We solve this optimization problem by appealing to a recently proposed preconditioned variant of the popular primal-dual method by Pock and Chambolle, which results in a sparse label propagation algorithm. This learning algorithm allows for a highly scalable implementation as message passing over the underlying data graph. By applying concepts of compressed sensing to the learning problem, we are also able to provide a transparent sufficient condition on the underlying network structure such that accurate learning of the labels is possible. We also present an implementation of the message passing formulation allows for a highly scalable implementation in big data frameworks.

And here we begin to see some of the subtlety of machine learning. The actual classifier we built works only because in the training examples for each character there were ones with different backgrounds--so it can figure out that background isn't the only distinguishing feature.

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being non-signalling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large number of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

Carter Wilkerson, 16, of Reno took on a Wendy's challenge to get 18 million retweets for free chicken nuggets for a year. On Tuesday, May 9, 2017, he beat the record set by Ellen DeGeneres. He will get the free nuggets. Carter Wilkerson, 16, of Reno, reached the top of the Twitter game Tuesday morning with the most retweets ever (3.441 million as of 11:24 a.m. And he'll get his chicken nuggets, plus $100,000 to a national charity.

In 1844, a laborer from a small town in southern Italy was put on trial for stealing "five ricottas, a hard cheese, two loaves of bread […] and two kid goats". The laborer, Giuseppe Villella, was reportedly convicted of being a brigante (bandit), at a time when brigandage -- banditry and state insurrection -- was seen as endemic. Villella died in prison in Pavia, northern Italy, in 1864. Villella's death led to the birth of modern criminology. Nearby lived a scientist and surgeon named Cesare Lombroso, who believed that brigantes were a primitive type of people, prone to crime.

It covers self-study tutorials and end-to-end projects on topics like: Loading data, visualization, modeling, algorithm tuning, and much more...

Supervised learning systems rely upon humans to label the incoming data -- at least to begin with -- in order for the systems to better predict how to classify future input data. Gmail's spam filter is a great example of this. When you label incoming mail as either spam or not spam, you're not only cleaning up your inbox, you're also training Gmail's filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future. According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they're rewarded.