Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual or tactile sensing, where unfortunately one fails to capture high-frequency interaction, while the other can be too delicate for large-scale data collection. In this work, we propose a data-centric approach to dynamic manipulation that uses an often ignored source of information: sound. We first collect a dataset of 25k interaction-sound pairs across five dynamic tasks using commodity contact microphones. Then, given this data, we leverage self-supervised learning to accelerate behavior prediction from sound. Our experiments indicate that this self-supervised 'pretraining' is crucial to achieving high performance, with a 34.5% lower MSE than plain supervised learning and a 54.3% lower MSE over visual training. Importantly, we find that when asked to generate desired sound profiles, online rollouts of our models on a UR10 robot can produce dynamic behavior that achieves an average of 11.5% improvement over supervised learning on audio similarity metrics.

Rattle and R deliver a very sophisticated data mining environment. Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package.

Life has been busy for the stars of "Sherlock" since the series premiered in 2010, with Steven Moffat and Mark Gatiss applying new London style and contemporary quirks to Arthur Conan Doyle's famous consulting detective. Its fourth season -- there have been breaks -- begins Sunday on PBS' "Masterpiece: Mystery!" Martin Freeman, the series' Dr. John Watson, has gone from a guy you might have seen on the British version of "The Office" or in "The Hitchhiker's Guide to the Galaxy" to playing Bilbo Baggins in three "Hobbit" movies and the hapless Lester Nygaard in the first season of FX's "Fargo," and hosting "Saturday Night Live." Benedict Cumberbatch, its Sherlock (also in the "Hobbit" movies, as the voice of Smaug) has, among other things, played Khan in "Star Trek Into Darkness," the title role in "Doctor Strange," codebreaker Alan Turing in "The Imitation Game" and Richard III in BBC's "The Hollow Crown" Shakespeare cycle; sung "Comfortably Numb" with Pink Floyd's David Gilmour at the Royal Albert Hall; and has become something of an international, official hot guy. Conan Doyle wrote 60 Holmes stories, but the world has deemed that insufficient, and many other hands have filled out the tale. Holmes is a useful mix of specific qualities and scant details -- an attitude, occupation and method as much as a full-fleshed, full-fledged character, and so familiar that even some characters not called Sherlock Holmes, like Hugh Laurie's Dr. House on "House," are recognizably him.

This book is a high quality statistical text with R as the software of choice. If you want to be comfortable with fundamental concepts in parallel with learning R, then this is the book for you. Having said this, you will love this book even if you have studied advanced statistics. The book also covers some advanced machine learning concepts such as support machine learning (SVM) and regularization.