Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters.

A leading centre for AI education will open up to the world, as Imperial launches its first Massive Open Online Courses with Coursera. The move allows anyone with an internet connection to learn from some of the world's top researchers in artificial intelligence (AI), machine learning and mathematics. Professor Alice Gast, President of Imperial, said: "AI has the potential to transform many sectors. It is wonderful to have world-leading Imperial experts providing this opportunity to such a broad audience. Many will benefit from this exciting curriculum on the machine learning and mathematics underpinning the rapid advances in AI."

About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

These are all introductory content and are the more popular ones you'll find.

A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and secondorder methods. Here we explore the alternative approach of putting exponential weights (EW) first. We show that many standard methods and their regret bounds then follow as a special case by plugging in suitable surrogate losses and playing the EW posterior mean. For instance, we easily recover Online Gradient Descent by using EW with a Gaussian prior on linearized losses, and, more generally, all instances of Online Mirror Descent based on regular Bregman divergences also correspond to EW with a prior that depends on the mirror map. Furthermore, appropriate quadratic surrogate losses naturally give rise to Online Gradient Descent for strongly convex losses and to Online Newton Step. We further interpret several recent adaptive methods (iProd, Squint, and a variation of Coin Betting for experts) as a series of closely related reductions to exp-concave surrogate losses that are then handled by Exponential Weights. Finally, a benefit of our EW interpretation is that it opens up the possibility of sampling from the EW posterior distribution instead of playing the mean. As already observed by Bubeck and Eldan (2015), this recovers the best-known rate in Online Bandit Linear Optimization.

This game-changing course will cover artificial intelligence tools in website, chatbot design and analytics which will help you to create website in minutes. I will teach you to easily create websites in the fastest time possible and customize your site look and feel according to your requirement in a simple drag-and-drop timeline by talking to chatbots. Why learn this course and how is this a differentiator? This course can change your life as a web developer or marketer. With no coding experience, you can create amazing looking websites and pave the path for unlimited designs and interchange content and play god.

In today's world, children as young as pre-schoolers have already started using tablets while top executive education programmes boast high-tech facilities where corporate leaders can learn in new ways. We have also seen the rise of e-learning and distance learning for many university degrees, with students learning online without ever having to step into a classroom.

Whenever you are about to be oppressed, you have a right to resist oppression: whenever you conceive yourself to be oppressed, conceive yourself to have a right to make resistance, and act accordingly. In proportion as a law of any kind--any act of power, supreme or subordinate, legislative, administrative, or judicial, is unpleasant to a man, especially if, in consideration of such its unpleasantness, his opinion is, that such act of power ought not to have been exercised, he of course looks upon it as oppression: as often as anything of this sort happens to a man--as often as anything happens to a man to inflame his passions,--this article, for fear his passions should not be sufficiently inflamed of themselves, sets itself to work to blow the flame, and urges him to resistance. Submit not to any decree or other act of power, of the justice of which you are not yourself perfectly convinced. If a constable call upon you to serve in the militia, shoot the constable and not the enemy;--if the commander of a press-gang trouble you, push him into the sea--if a bailiff, throw him out of the window. If a judge sentence you to be imprisoned or put to death, have a dagger ready, and take a stroke first at the judge.

Hoy traemos a este espacio el nuevo número de iJET International Journal of Emerging Technologies in Learning el último de 2017 Vol 12, No 12 (2017) Table of Contents Papers Application of Digital Music Technology in Music Pedagogy Peiwei Zhang, Xin Sui Music Solfeggio Learning Platform Construction and Application Qiao Zhou, Baihui Yan The Effects of the CALL Model on College English Reading Teaching Dan Zhang, Xiaoying Wang The Construction of Intelligent English Teaching Model Based on Artificial Intelligence Design and Implementation of English Reading Examination System Based on WEB Platform Lan Guo, Zhiyu Zhao, Lu Bai, Jing Lv, Xin Zhao On Spoken English Phoneme Evaluation Method Based on Sphinx-4 Computer System Computer Multimedia Assisted English Vocabulary Teaching Courseware Multi-Interactive Teaching Model of College English in Computer Information Technology Environment Design Flow of English Learning System Based on Item Response Theory Yuemei Liu, Xuetao Zhao Application of Kinect Technology in Blind Aerobics Learning Short Papers Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis Haiyun Li, Xuebo Zhang, Junhui Wang Evaluation of Sports Visualization Based on Wearable Devices Application of Data Mining in Library-Based Personalized Learning A Personalized Recommender System Based on Library Database Music Learning Based on Computer Software Baihui Yan, Qiao Zhou International Journal of Emerging Technologies in Learning.

Alex Irpan, a software engineer at Google, wrote an excellent article on the current difficulties of getting deep reinforcement learning to work. For example, even after weeks of optimizing hyperparameters and explotation-exploration rates, these models are still highly sensitive to initial conditions. A 30% failure rate is seen as "working." Irpan makes the argument that most attempts with deep RL fail but no one talks about it publicly, we only see the few cases where the problems are simplified enough to be feasible. This is still a new field - the breakthrough Atari DQN paper was published only 3 years ago - so there is plenty of room for more research and advancement.