In a seminal paper Abadie, Diamond, and Hainmueller [2010] (ADH), see also Abadie and Gardeazabal [2003], Abadie et al. [2014], develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units. The method constructs a set of weights such that selected covariates and pre-treatment outcomes of the treated unit are approximately matched by a weighted average of control units (the synthetic control). The weights are restricted to be nonnegative and sum to one, which is important because it allows the procedure to obtain unique weights even when the number of lagged outcomes is modest relative to the number of control units, a common setting in applications. In the current paper we propose a generalization that allows the weights to be negative, and their sum to differ from one, and that allows for a permanent additive difference between the treated unit and the controls, similar to difference-in-difference procedures. The weights directly minimize the distance between the lagged outcomes for the treated and the control units, using regularization methods to deal with a potentially large number of possible control units.

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Some in-video questions and practice quizzes will help you to review the material, with no effect on the final grade. A mandatory peer-graded assignment is also present, for weeks from 1 to 9, and it will expand on the lectures' topics, letting you reach a deeper understanding. The nine peer-graded assignments will make up for 50% of the grade, while the other half will come from a final exam, after the last lecture. In the tutorial we will use the 3x3 pebble game to understand the essential concepts of Monte Carlo techniques (detailed balance, irreducibility, and a-periodicity), and meet the celebrated Metropolis algorithm. Finally, the homework session will let you understand some useful aspects of Markov-chain Monte Carlo, related to convergence and error estimations.

If you went to school in the US in the early 1990s, chances are you have fond memories of playing the computer game The Oregon Trail. Now, schoolchildren around the world will be able to replicate that experience thanks to a new Minecraft: Education Edition integration. Before you fire up your version of Minecraft to download this add-on, there's a catch: Because it's specifically an educational endeavor (in partnership with publisher Houghton Mifflin Harcourt), the add-on is only available through the education edition of the game. This version of The Oregon Trail has been expanded with learning activities and the freedom to create new paths for the game and form their own 19th century communities within it. Previously, the game has worked with the Roald Dahl estate on a writing competition for elementary and middle school students.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

In this episode of the ARCHITECHT Show, Elastic founder and CEO Shay Banon talks about the evolution of Elasticsearch -- from an open source side project (the first iteration was a recipe-search app for his wife) to popular big data tool to the core of a company worth nearly a billion dollars. He also shares his thoughts and strategies on the growth of Elastic, which, somewhat under the radar, has expanded to include multiple products and employ hundreds of people around the world. In this episode of the ARCHITECHT AI Show, Derrick Harris speaks with Jeremy Howard and Rachel Thomas of Fast.ai, Among other things, Howard and Thomas discuss the promise of deep learning and early student successes (including Hot Dog, Not Hot Dog app from Silicon Valley), as well as the threat of job losses from AI and how seriously we should take Elon Musk's AI warnings. AIMatter is based in Belarus and built an app, called Fabby, that lets users add effects to their selfies.

Everyone wants to minimize losses and maximize profits. AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Thanks to Deep Learning and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a developer, this course gives you a great opportunity to expand your skillset.

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

The intervention of digital is transforming the way we live. Be it to plan a holiday, watch a movie, eat at a restaurant, or to avail any lifestyle or home services, an increasing number of people are getting heavily dependent on technology. Given the data-rich and data-driven playgrounds of artificial intelligence and machine learning, several skilled human tasks have already been replaced by automated platforms. That's the impact technology is making on our lives. Education is one of the sectors ripe for technological disruption.

Nothing reveals as much about a society, and its future, as its high schools. Yet amid accelerating change -- widening inequality, unprecedented globalization and technological advances -- they've woefully lagged behind. There are, of course, exceptions. Follow OZY's special series High School, Disrupted to find out about the global leaders, cutting-edge trends and big ideas reimagining secondary education -- for the better. From Siri handling our schedules to smart cars driving themselves, artificial intelligence (AI) has turned our world upside down -- except in education.