The Machine Learning Handbook is an eBook that I wrote comprising of my notes from Stanford's online Machine Learning course on Coursera.

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Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo). He has since turned to work on neuroscience full-time, founded the Redwood Center for Theoretical Neuroscience (formerly the Redwood Neuroscience Institute) in 2002, founded Numenta in 2005 and published On Intelligence describing his memory-prediction framework theory of the brain. In 2003 he was elected as a member of the National Academy of Engineering "for the creation of the hand-held computing paradigm and the creation of the first commercially successful example of a hand-held computing device." Hawkins also serves on the Advisory Board of the Secular Coalition for America and offers advice to the coalition on the acceptance and inclusion of nontheism in American life. Andrew Yan-Tak Ng is Chief Scientist at Baidu Research in Silicon Valley.

Cameras are used routinely by police across the US to identify citizens, their faces cross-matched against databases of suspects and past criminals. Yet researchers claim there is too little scrutiny of how these tools work, and have found inherent racial bias in the system. So does a sophisticated, visual analysis tool reflect human prejudice and if so, who does that effect? "Studies indicate there's racial bias in the software," said Jonathan Frankle, staff technologist at Georgetown Law School. Working with law fellow Clare Garvie, Frankle has requested public information from more than 100 police departments across the country.

Every spring, inventor Dean Kamen hosts his own sort of March Madness: a spectacle in which high-school students compete in events around the world surrounded by cheerleaders, music and entertainment. But rather than playing basketball, they're focused on building robots, an effort that culminates at the championship in St. Louis at the end of this month. Mr. Kamen, 65, is known for coming up with the Segway (the two-wheeled electric vehicle), the iBot (a stair-climbing wheelchair) and a portable dialysis machine. He considers the First Robotics Competition, now in its 25th season, one of his best ideas yet. While many young adults look up to athletes and actors as their heroes, Mr. Kamen hopes his competition--designed like a sports event, with regional brackets--will show them that there are other kinds of stars. "If you think that you can be a superstar in sports or entertainment and make the really big bucks, imagine being a superstar in tech," he says.

We often get questions about whether to use Python or R – and we've come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

We often get questions about whether to use Python or R – and we've come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools.

Some foundations of statistical science have been questioned recently, especially the use and abuse of p-values. See also this article published in FiveThirtyEight.com. Statistical tests of hypotheses rely on p-values and other mysterious parameters and concepts that only the initiated can understand: power, type I error, type II error, or UMP tests, just to name a few. Pretty much all of us have had to learn this old stuff (pre-dating the existence of computers) in some college classes. Sometimes results from a statistical test will be published in a mainstream journal - for instance about whether or not global warming is accelerating - using the same jargon that few understand, and accompanied by misinterpretations and flaws in the use of the test itself.

As we enter into the big data age and an avalanche of images have become readily available, recognition systems face the need to move from close, lab settings where the number of classes and training data are fixed, to dynamic scenarios where the number of categories to be recognized grows continuously over time, as well as new data providing useful information to update the system. Recent attempts, like the open world recognition framework, tried to inject dynamics into the system by detecting new unknown classes and adding them incrementally, while at the same time continuously updating the models for the known classes. incrementally adding new classes and detecting instances from unknown classes, while at the same time continuously updating the models for the known classes. In this paper we argue that to properly capture the intrinsic dynamic of open world recognition, it is necessary to add to these aspects (a) the incremental learning of the underlying metric, (b) the incremental estimate of confidence thresholds for the unknown classes, and (c) the use of local learning to precisely describe the space of classes. We extend three existing metric learning algorithms towards these goals by using online metric learning. Experimentally we validate our approach on two large-scale datasets in different learning scenarios. For all these scenarios our proposed methods outperform their non-online counterparts. We conclude that local and online learning is important to capture the full dynamics of open world recognition.

Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces. However, the problem of unwrapping or unfolding manifolds has received relatively little attention within the density ridge approach, despite being an integral part of manifold learning in general. This paper proposes two novel algorithms for unwrapping manifolds based on estimated principal curves and surfaces for one- and multi-dimensional manifolds respectively. The methods of unwrapping are founded in the realization that both principal curves and principal surfaces will have inherent local maxima of the probability density function. Following this observation, coordinate systems that follow the shape of the manifold can be computed by following the integral curves of the gradient flow of a kernel density estimate on the manifold. Furthermore, since integral curves of the gradient flow of a kernel density estimate is inherently local, we propose to stitch together local coordinate systems using parallel transport along the manifold. We provide numerical experiments on both real and synthetic data that illustrates clear and intuitive unwrapping results comparable to state-of-the-art manifold learning algorithms.