Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Udacity students are constantly impressing us with their skill, ingenuity, and their knowledge of the most obscure features in Slack. Here are 6 blog posts that will astound you, and 1 think-piece that will blow your mind. Sujay's managed his data in a few clever ways for the traffic sign classifier project. First, he converted all of his images to grayscale. Then he skewed and augmented them.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field.

Whether it's ads that predict our buying behaviors or sophisticated image searching, machine learning is here and in our technology. Get into the new wave with the Deep Learning and Artificial Intelligence Introductory Bundle. This bundle dives into the powerful algorithms that produce our most sophisticated technology. More and more companies are relying on the concepts of deep learning and machine learning to produce machine responses that evolve and adapt to human actions -- just think of your Netflix recommendations or suggested contacts on Facebook. With four courses on Python, data science and more, you'll set yourself apart from the pack with a deeper understanding of the latest revolution sweeping current technology.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field. "If you think about online courses where there may be hundreds of students, it's currently very difficult for a professor or instructor to maybe get a good grasp on how students not only are performing, but are feeling about the material…as they're lecturing or a video's playing," she said. "Virtual avatars and chatbots…have the ability to assess that on an individual level, and if the student seems stuck then maybe you can replay part of the video.

Andrew Yan-Tak Ng (Chinese: 吴恩达; born 1976) is a Chinese American computer scientist. He is the chief scientist at Baidu Research in Silicon Valley. In addition, he is an adjunct professor (formerly associate professor) at Stanford University. Ng is also the co-founder and chairman of Coursera, an online education platform. Ng researches primarily in machine learning and deep learning.

Artificial intelligence is the future of computer science and technology. Its impact will be almost immeasurable. The field is wide open today, with so much to learn, and so many ways to contribute. We have collaborated with industry leaders to bring you cutting-edge curriculum covering topics such as search and optimization; logic, reasoning, and planning; building models of probability; natural language processing; computer vision, and much more. You'll master skills and tools used by the most innovative AI teams across the globe as you delve into specializations, and gain experience solving real-world challenges.

Reservoir Computing is a bio-inspired computing paradigm for processing time-dependent signals (Jaeger and Haas 2004; Maass, Natschläger, and Markram 2002). The performance of its hardware implementation (see e.g. (Soriano et al. 2015) for a review) is comparable to state-of-the-art digital algorithms on a series of benchmark tasks.The major bottleneck of these implementation is the readout layer, based on slow offline post-processing. Several analogue solutions have been proposed (Smerieri et al. 2012; Duport et al. 2016; Vinckier et al. 2016), but all suffered from noticeable decrease in performance due to added complexity of the setup. Here we propose the online learning approach to solve these issues. We present an experimental reservoir computer with a simple analogue readout layer, based on previous works, and show numerically that online learning allows to disregard the added complexity of an analogue layer and obtain the same level of performance as with a digital layer. This work thus demonstrates that online training allows building high-performance fully-analogue reservoir computers, and represents an important step towards experimental validation of the proposed solution.

A challenge for mining large-scale streaming data overlooked by most existing studies on online learning is the skew-distribution of examples over different classes. Many previous works have considered cost-sensitive approaches in an online setting for streaming data, where fixed costs are assigned to different classes, or ad-hoc costs are adapted based on the distribution of data received so far. However, it is not necessary for them to achieve optimal performance in terms of the measures suited for imbalanced data, such as F-measure, area under ROC curve (AUROC), area under precision and recall curve (AUPRC). This work proposes a general framework for online learning with imbalanced streaming data, where examples are coming sequentially and models are updated accordingly on-the-fly. By simultaneously learning multiple classifiers with different cost vectors, the proposed method can be adopted for different target measures for imbalanced data, including F-measure, AUROC and AUPRC. Moreover, we present a rigorous theoretical justification of the proposed framework for the F-measure maximization. Our empirical studies demonstrate the competitive if not better performance of the proposed method compared to previous cost-sensitive and resampling based online learning algorithms and those that are designed for optimizing certain measures.