After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.

After introducing the scikit-learn API, we'll use it to explore the basic categories of machine-learning problems and related topics such as feature selection and model validation and practice applying these tools to real-world datasets.

I've decided that I'm going to get really good at using R. What is R, you might ask? R is a programming language for originally created for statistical analysis, and my interest in it means I've apparently come to terms with the fact that I'm the type of person that jocks gave wedgies and swirlies to in high school. Why am I interested in R? Well, R is a programming language that will enable machine learning. I'm not talking about the robots-flipping-pancakes type of machine learning which -- while cool -- is not what comes to mind when thinking about statistics. Rather, the type of machine learning I'm talking about is where a user programs a computer to learn without being explicitly programmed to do so.

So far I have talked about decision trees and ensembles. But I hope, I have made you understand the logic behind these concepts without getting too much into the mathematical details. In this post lets get into action, I will be implementing the concepts that we learned in these two blog posts. The only concept that I haven't discussed about is SVM. I suggest you to watch Professor Andrew Ng's week 7 videos on Coursera.

The students have qualified for this week's championship by beating others at state and regional competitions throughout the school year. During the competition, the robots work to outmaneuver one another by hurling boulders through the window of a tower and attempting to scale their opponent's tower. "It's really spectacular and kind of a rush," said Thomas Mills, a high school senior from Florissant. Another league gives kindergarten through eighth-grade students a real-world problem to solve. This year's theme is solid waste.

Human Rights Watch and Harvard Law School's International Human Rights Clinic added to the urgency of the meeting by issuing a report calling for a complete ban on autonomous killer robots. It would undermine human dignity to be killed by a machine that can't understand the value of human life. "It would undermine human dignity to be killed by a machine that can't understand the value of human life," she says. The next meeting in Geneva is set for December, when a U.N. group will decide whether to formally start developing new international law governing killer robots.

The test is broken down into four types of exercises: vocabulary (picking out real words from a list of fake words), cloze passages (completing sentences with missing words), listening (transcribing a spoken text), and "speaking" (reciting a written prompt). Settles points to research indicating that many of these tasks are "highly predictive of overall language ability"--people who perform well on them tend to be proficient users of English. Some sentences are culled from novels, news articles, or encyclopedias in the public domain. Settles and his team spent a lot of time designing natural language–processing algorithms that could read through the giant corpus of texts available online, automatically create appropriate test items, and then calibrate their difficulty. Because the test is "computer-adaptive," it becomes harder or easier as you give right or wrong answers, allowing your English level to be quickly pinpointed.

I first confess that I will be plugging my alma mater in the process, and I come from the viewpoint of traditional, rigorous statistics training more than what is broadly and commonly regarded as'analytics'. Internationally, Stanford certainly stands out as the powerhouse of statistics programs, with well-respected faculty like Hastie and Tibshirani providing the cutting edge. As an aside, you might be interested in the Statistical Learning and Data Mining short course that Hastie et al open to the public once every 6 months or so (independently of Stanford I believe, but do check). It runs for 2 days, and could give you a flavor. You are probably right to identify India and Singapore as the hubs for analytics in Asia.

Technologies that most, if not every, technologist is well aware of. Maybe a little more nuanced for some to track? The beauty of being a budding technologist is that the landscape is constantly changing and forever in flux. What was once a herculean task of reading magazines and books with the hopes of finding the necessary information to connect the dots has been trivialized to a simple "online search" or a quick question to your phone. With the general accessibility of the internet, the ability to track and understand the technology landscape has simplified.

The summer school occurs at the first two weeks of the Third Frederick Jelinek Memorial Summer Workshop. The North American Chapter of the Association for Computational Linguistics (NAACL) is again offering an exciting summer school opportunity for a limited number of undergraduate students interested in the field of Human Language Technology. The 2016 Jelinek Summer Workshop will be held from Monday, June 13 to Friday, August 5, 2016 at the Johns Hopkins University in Baltimore, Maryland. The first two weeks of the workshop (from June 13 to 24) is a summer school that provides general introductions to the major areas of study within the field of Human Language Technology (e.g., Natural Language Processing, Automatic Speech Recognition, Machine Translation, Information Retrieval) as well as sessions on specialized research topics of current interest in the field. The NAACL scholarship is used to support undergraduate students to participate in the two-week summer school.