Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Robots conjure images of some distant future – a crisp, white-walled existence. Except that time is now. Last month, one leading futurologist predicted that the global robot population, already more than 57 million, will outnumber humans by 2048, and that's a conservative estimate. Automation is already ingrained in everything from warehouse management to selling financial products. Workers are worried that they are going to find themselves replaced in the jobs market; employer services provider ADP recently surveyed 1,300 working adults and found that a third believe their job will be automated within the next 10 years.

With this course, you'll have the keys to the entire Python Data Science kingdom!

One of the fundamental research challenges in network science is centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this article, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been recently proposed based on this approach, it is still unknown how general is the game-theoretic approach to centrality and what distinguishes some game-theoretic centralities from others. In this article, we attempt to answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every possible centrality measure can be obtained following the game-theoretic approach. Furthermore, we study three natural classes of game-theoretic centrality, and prove that they can be characterized by certain intuitive properties pertaining to the well-known notion of Fairness due to Myerson.

In this paper, we introduce a novel algorithm to solve projected model counting (PMC). PMC asks to count solutions of a Boolean formula with respect to a given set of projected variables, where multiple solutions that are identical when restricted to the projected variables count as only one solution. Our algorithm exploits small treewidth of the primal graph of the input instance. It runs in time $O({2^{2^{k+4}} n^2})$ where k is the treewidth and n is the input size of the instance. In other words, we obtain that the problem PMC is fixed-parameter tractable when parameterized by treewidth. Further, we take the exponential time hypothesis (ETH) into consideration and establish lower bounds of bounded treewidth algorithms for PMC, yielding asymptotically tight runtime bounds of our algorithm.

Nowadays, it is well accepted that formal ontologies are commonly used as support for the axiomatisation of the knowledge describing a domain of interest. In particular, for domains in the engineering area where concepts are well mastered by the different stakeholders, ontologies play a major role for knowledge exchange and heterogeneity reduction. Meanwhile, we observe that defining a formal framework for integrating both ontologies represented by knowledge models and design models of particular systems did not draw the attention of many researchers in system engineering. Approaches like those of [3][4][5][7][9][12] supporting the integration of both ontologies and design models contribute to strengthen these design models by offering the capability to design models to borrow knowledge from ontologies, using a particular annotation relationship. As a consequence, the design models are enriched and strengthened with axioms, theorems or invariants issued from the used ontologies. This paper presents a summary of the work achieved in the context of the French ANR IMPEX research project. Ontologies are formalised as theories with axioms, theorems and reasoning rules. Event-B [1] has been chosen as the ground formal modelling technique for all our developments.

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times. Some authors have relaxed such requirements by supplementing the model with an handcrafted physical simulator. Still, the resulting methods are unable to automatically learn new complex environments and to understand physical interactions within them. In this work, we demonstrated for the first time learning such predictors directly from raw visual observations and without relying on simulators. We do so in two steps: first, we learn to track mechanically-salient objects in videos using causality and equivariance, two unsupervised learning principles that do not require auto-encoding. Second, we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that in all such cases it is possible to learn reliable extrapolators of the object trajectories from raw videos alone, without any form of external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.

Over the past years, distributed representations have proven effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey is focused on semantic representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their main limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and provides an analysis of five important aspects: interpretability, sense granularity, adaptability to different domains, compositionality and integration into downstream applications.

Automatic sleep staging is a challenging problem and state-of-the-art algorithms have not yet reached satisfactory performance to be used instead of manual scoring by a sleep technician. Much research has been done to find good feature representations that extract the useful information to correctly classify each epoch into the correct sleep stage. While many useful features have been discovered, the amount of features have grown to an extent that a feature reduction step is necessary in order to avoid the curse of dimensionality. One reason for the need of such a large feature set is that many features are good for discriminating only one of the sleep stages and are less informative during other stages. This paper explores how a second feature representation over a large set of pre-defined features can be learned using an auto-encoder with a selective attention for the current sleep stage in the training batch. This selective attention allows the model to learn feature representations that focuses on the more relevant inputs without having to perform any dimensionality reduction of the input data. The performance of the proposed algorithm is evaluated on a large data set of polysomnography (PSG) night recordings of patients with sleep-disordered breathing. The performance of the auto-encoder with selective attention is compared with a regular auto-encoder and previous works using a deep belief network (DBN).

Many data science issues have to be addressed through unbalanced datasets. Indeed, it may be quite affordable to gather data on the representatives of a given pathology in medicine, or positive operating scenarios of machines in the industry [1]. The related complementary occurrences are, by contrast, scarce and/or expensive to raise. The practice of One-Class Classification (OCC) has been developed within this consideration [1, 2]. One-class classifiers are trained on a single class sample, in the possible presence of a few counterexamples. The related issue consists of understanding and isolating a given class from the rest of the universe. The resulting model allows to predict target (or positive) patterns and to reject outlier (or negative) ones. One-Class Support Vector Machine (OCSVM) is a popular OCC method [3, 4]. Statistics-based techniques such as Gaussian models and Kernel Density Estimation (KDE) [5] are also commonly considered as respectively parametric and nonparametric approaches to estimate a sample distribution.