In a world dominated by data, Data Science is the ladder to building a promising career in unique and challenging job positions. Kickstarting your career in Data Science is now easier than ever thanks to the vast pool of online platforms offering Data Science courses. These courses are specially designed to walk you through the concepts and intricacies of Data Science. But, do you know the exact way to climb the ladder? Fret not, for we're here to show you how! So, let's begin, shall we?

IMAGE: This is a screenshot of the Science Search interface. In this case, the user did an image search of nanoparticles. As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building.

IBM developer Dale Lane, who helped create the educational tool Machine Learning for Kids, believes that while the "most critical aspect of AI education is helping teachers to improve their own skills and educate our children more effectively", this continues to be overlooked. He shares Professor Luckin's frustration over the lack of progress so far.

In this case, the user performed an image search for nanoparticles. As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at Berkeley Lab's Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments.

People who want to be inspired watch TED Talks, largely because of the stories they tell. These stories spark imagination and motivate audiences to think in new ways. TED Talks redefine knowledge and point to potential. Tufekci explains how we are routinely using computers to make subjective decisions like hiring new employees, releasing criminals from prison, and even identifying handwriting. Machine learning has made it possible for computers to make decisions about things we might not have disclosed – for example, about future likelihood of depression or pregnancy.

We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee $O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our algorithms rely on a novel SDP relaxation for the steady-state distribution of the system. Crucially, and in contrast to previously proposed relaxations, the feasible solutions of our SDP all correspond to "strongly stable" policies that mix exponentially fast to a steady state.

With the recent success of embeddings in natural language processing, research has been conducted into applying similar methods to code analysis. Most works attempt to process the code directly or use a syntactic tree representation, treating it like sentences written in a natural language. However, none of the existing methods are sufficient to comprehend program semantics robustly, due to structural features such as function calls, branching, and interchangeable order of statements. In this paper, we propose a novel processing technique to learn code semantics, and apply it to a variety of program analysis tasks. In particular, we stipulate that a robust distributional hypothesis of code applies to both human- and machine-generated programs. Following this hypothesis, we define an embedding space, inst2vec, based on an Intermediate Representation (IR) of the code that is independent of the source programming language. We provide a novel definition of contextual flow for this IR, leveraging both the underlying data- and control-flow of the program. We then analyze the embeddings qualitatively using analogies and clustering, and evaluate the learned representation on three different high-level tasks. We show that with a single RNN architecture and pre-trained fixed embeddings, inst2vec outperforms specialized approaches for performance prediction (compute device mapping, optimal thread coarsening); and algorithm classification from raw code (104 classes), where we set a new state-of-the-art.

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings process. In this paper, we investigate if graph embeddings are approximating something analogous with traditional vertex level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation from five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated by the embedding space, allowing key insight into how graph embeddings create good representations.

Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a complex network of interacting biochemical components that evolve at different timescales. In this paper, we show that by equipping tabular and deep reinforcement learning agents with a synaptic model that incorporates this biological complexity (Benna & Fusi, 2016), catastrophic forgetting can be mitigated at multiple timescales. In particular, we find that as well as enabling continual learning across sequential training of two simple tasks, it can also be used to overcome within-task forgetting by reducing the need for an experience replay database.

Roundtable Analytics Inc., an AI/ML-based software company focused on improving the operational and financial performance of Emergency Departments (EDs), expanded its team of data scientists with the recent appointment of Michael Hyman, Ph.D., as vice president. Michael (Mike) Hyman has had a distinguished academic career, graduating with honors from the University of North Carolina with a Bachelor of Science in mathematics and a minor in chemistry. Pursuing his passion for the application of data science, Mike went on to earn both an M. Stat and Ph.D. in statistics from the University of Florida, one of the premier statistics graduate programs in the U.S. During his studies at UF, he was named a National Science Foundation Fellow, receiving an IGERT award for interdisciplinary training, and shared an office with the future founders of Roundtable Analytics Inc. Prior to joining Roundtable, Mike spent over five years working with the U.S. Department of Agriculture, performing statistical and geospatial research and developing the statistical methodology for projects such as the U.S. Census of Agriculture. Previously, during his spare time, Mike organized and completed a charity bike ride across the United States and is delighted to now return to his hometown of Raleigh, North Carolina.