In first part we explored the statistical model underlying the machine learning problem, and used it to formalize the problem in terms of obtaining the minimum generalization error. By noting that we cannot directly evaluate the generalization error of an ML model, we continued in the second part by establishing a theory that relates this elusive generalization error to another error metric that we can actually evaluate, which is the empirical error. That is: the generalization error (or the risk) $R(h)$ is bounded by the empirical risk (or the training error) plus a term that is proportionate to the complexity (or the richness) of the hypothesis space $ \mathcal{H} $, the dataset size $N$, and the degree of certainty $1 - \delta$ about the bound. Starting from this part, and based on this simplified theoretical result, we'll begin to draw some practical concepts for the process of solving the ML problem. We'll start by trying to get more intuition about why a more complex hypothesis space is bad.

Inverse classification is the process of perturbing an instance in a meaningful way such that it is more likely to conform to a specific class. Historical methods that address such a problem are often framed to leverage only a single classifier, or specific set of classifiers. These works are often accompanied by naive assumptions. In this work we propose generalized inverse classification (GIC), which avoids restricting the classification model that can be used. We incorporate this formulation into a refined framework in which GIC takes place. Under this framework, GIC operates on features that are immediately actionable. Each change incurs an individual cost, either linear or non-linear. Such changes are subjected to occur within a specified level of cumulative change (budget). Furthermore, our framework incorporates the estimation of features that change as a consequence of direct actions taken (indirectly changeable features). To solve such a problem, we propose three real-valued heuristic-based methods and two sensitivity analysis-based comparison methods, each of which is evaluated on two freely available real-world datasets. Our results demonstrate the validity and benefits of our formulation, framework, and methods.

Since the dawn of the industrial revolution, the pace of change in the world is so fast that if someone from medieval times would come back, he will want to die again because he will not be able to adjust in this new world which is trying to turn science fiction into reality. Driverless cars, drone deliveries; every year is bringing something new for us. Here are those five technologies or ideas which will change the world in the years to come. In the modern post-industrial economy, investment on higher education gives more dividends than even capital. It has helped many to get out of the vicious circle of poverty across all continents. Attaining education from the world's top universities has been a dream for many people but few are able to achieve due to many constraints.

Do you think Hollywood has lost touch with American values? Do you think Hollywood has lost touch with American values? The contentious presidential campaign was filled with accusations of elitism and bias by the media -- from the news to entertainment. Many supporters of Donald J. Trump saw his victory as a repudiation of the so-called liberal elite. So as 2017 begins, we ask: Is Hollywood representing all Americans? Are Hollywood values out of sync with American values? It's the start of a conversation we'll have all year with Hollywood's creators, consumers and observers. Most of all, we want to hear from you. Is Hollywood out of touch with your America? Here's what our critics and writers have to say: KENNETH TURAN on potent Hollywood visions that helped elect Trump TV's affluent bubble: MARY McNAMARA on Hollywood's reluctance to deal with class issues Fear of the powerful woman: JUSTIN CHANG on working women and men still behaving badly Realistic or cliche?: JEFFREY FLEISHMAN on ...

Google Home and Amazon's Echo are the most famous, but a whole raft of these gadgets is preparing to flood the market. One of the most advanced will likely come from Baidu, the Chinese tech giant that, like Google, began as a search engine and now has its tendrils in all sorts of digital and physical spaces. Andrew Ng, Baidu's chief AI scientist, calls these devices "conversational computers," and he's a key reason any of them have learned to talk in the first place. A former AI researcher at Stanford, Ng is best known for spearheading the Google Brain initiative, an ambitious artificial-intelligence project that helped advance Silicon Valley's understanding of deep-learning techniques. Instead of being programmed to respond to specific actions, a deep learning system is fed massive amounts of data from which it is able to discern patterns over time, loosely mimicking how the human mind absorbs information. Ng's system at Google famously figured out what a cat looks like after scanning millions of online images.

In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.

The McDonald's on the corner of Third Avenue and 58th Street in New York City doesn't look all that different from any of the fast-food chain's other locations across the country. Inside, however, hungry patrons are welcomed not by a cashier waiting to take their order, but by a "Create Your Taste" kiosk – an automated touch-screen system that allows customers to create their own burgers without interacting with another human being. It's impossible to say exactly how many jobs have been lost by the deployment of the automated kiosks – McDonald's has been predictably reluctant to release numbers – but such innovations will be an increasingly familiar sight in Trump's America. Once confined to the pages of futuristic dystopian fictions, the field of robotics promises to be the most profoundly disruptive technological shift since the industrial revolution. While robots have been utilized in several industries, including the automotive and manufacturing sectors, for decades, experts now predict that a tipping point in robotic deployments is imminent – and that much of the developed world simply isn't prepared for such a radical transition.

In an age where everything is changing –and changing fast– it's easy to forget how much we've progressed. While we may not have floating cars or robotic teachers, we are on the brink of some very exciting and dramatic developments across all industries. As one of the principal drivers of progression, it's no surprise that learning –and education in general– has been a focus of technological advances. While eLearning is not a new concept, its popularity is increasing, especially as technology becomes more affordable. A big barrier for eLearning is the cost of developing content.

Matthew Peterson is a pretty inspirational guy. As a dyslexic child he found math class difficult, so as an adult he resolved to totally change the way math is taught. After completing his studies in biology, electrical engineering, and Chinese language and literature at the University of California, Irvine, Peterson co-founded the nonprofit MIND Research Institute and set about developing "Spatial Temporal (ST) Math," a computer game-based method of teaching that doesn't rely on language as a medium. Instead it uses spatial-temporal reasoning--the ability to move stuff around in your mind and work out how it fits together. Proponents point to recent findings in neuroscience and education research--showing that early music training can enhance spatial-temporal reasoning, for example--as justification for this shift.

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.