Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians. Many times, I have interacted with statisticians who think that anyone not calling himself statistician, knows nothing or little about statistics; see my recent bio published here, or visit the LinkedIn profiles of many data scientists, to debunk this myth. Any statistical technique that is not in their old books are considered heretical at best, or non-statistic at worst, or most of the time, not understood.

We learn a lot about objects by manipulating them: poking, pushing, prodding, and then seeing how they react. We obviously can't do that with videos -- just try touching that cat video on your phone and see what happens. But is it crazy to think that we could take that video and simulate how the cat moves, without ever interacting with the real one? Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently done just that, developing an imaging technique called Interactive Dynamic Video (IDV) that lets you reach in and "touch" objects in videos. Using traditional cameras and algorithms, IDV looks at the tiny, almost invisible vibrations of an object to create video simulations that users can virtually interact with.

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a is our selection for the picture of the week. How to build a search engine - Part 2: Configuring elasticsearch Generative Adversarial Networks Explained in Layman Terms Curriculum Guidelines for Undergraduate Programs in Data Science The Perceptron Algorithm explained with Python code Great list of resources: data science, visualization, machine learn... Great list of resources: data science, visualization, machine learn... ALDI – New Paradigm for Integrating Marketing Analytics with Data S... Want to know how to choose Machine Learning algorithm? Quantifying Probabilities for Gambling System Strategies An Intro to Predictive Analytics: Can I predict the future?

Google wants to apply its machine learning technology to NHS patient data within the next five years, TechCrunch reports. The search giant's London-based artificial intelligence research lab, DeepMind, announced a partnership with the Royal Free NHS Trust in London in February but the full extent of the arrangement is only just becoming clear. A Memorandum of Understanding (MoU) between DeepMind and the Royal Free shows that the pair envisage a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximizing the potential to work on genuinely innovative and transformational projects." The MoU -- obtained via a Freedom of Information (FoI) request from New Scientist -- states that DeepMind hopes to gain access to "data for machine learning research under appropriate regulatory and ethical approvals" within the next five years. Machine learning -- a subfield of computer science that gives computers the ability to learn without being explicitly programmed -- has the potential to speed up patient diagnosis and optimise their treatments.

Problems of this nature occur in fields as diverse as business, medicine, astrophysics, and public policy. Why estimate f? How do we estimate f? Suppose we observe and for We believe that there is a relationship between Y and at least one of the X's. We can model the relationship as Where f is an unknown function and ε is a random error with mean zero. Why Do We Estimate f? Statistical Learning, and this course, are all about how to estimate f. The term statistical learning refers to using the data to "learn" f. Why do we care about estimating f? There are 2 reasons for estimating f, Prediction and Inference.

Type 1 diabetes (T1D) is a metabolic disease characterised by uncontrolled blood glucose levels, due to the absence or malfunction of insulin. The Artificial Pancreas (AP) system aims to simulate the function of the physiological pancreas and serve as an external automatic glucose regulation system. AP combines a continuous glucose monitor (CGM), a continuous subcutaneous insulin infusion (CSII) pump and a control algorithm which closes the loop between the two devices and optimises the insulin infusion rate. An important challenge in the design of efficient control algorithms for AP is the use of the subcutaneous route both for glucose measurement and insulin infusion (sc-sc route); this introduces delays of up to 30 minutes for sc glucose measurement and up to 20 minutes for insulin absorption. Thus, a total delay of almost one hour restricts both monitoring and intervention in real time. Moreover, glucose is affected by multiple factors, which may be genetic, lifestyle and environmental. With the improvement in sensor technology, more information can be provided to the control algorithm (e.g. more accurate glucose readings and physical activity levels); however, the level of uncertainty remains very high. Last but not least, one of the most important challenges emerges from the high inter- and intra-patient variability, which dictate personalised insulin treatment. Along with hardware improvements, the challenges of the AP are gradually being addressed with the development of advanced algorithmic strategies; the strategies most investigated clinically are the Proportional Integral Derivative (PID) [1], the Model Predictive Controller (MPC) [2]-[7] and fuzzy logic (e.g.

Hollywood actor Morgan Freeman has provided the voice for an AI assistant created by Facebook's Mark Zuckerberg. Mr Zuckerberg said he asked the actor, who was chosen by the public, after an awards ceremony earlier this month. The Facebook co-founder coded the AI assistant - called Jarvis, after the butler in Iron Man - for his home. If he decides to release it to the public, people would relate differently to a famous voice than more robotic sounding assistants, tech experts said. Mr Zuckerberg asked his Facebook followers to pick the voice after building artificial intelligence to help him around the house.

We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.

We feel there's precious little research being done around machine learning - so we'd like to urge you to participate in a developer survey that features an exclusive section on ML. The State of the Developer Nation survey tracks the most important trends across not just ML, but also mobile, IoT, desktop, web, cloud, and AR/VR - and presents the key insights in the form of a free report (due February). So take some time to participate - it's a worthwhile effort. You'll learn something new, and you'll have the chance to win a prize like a Data to Insights MIT course on Data Science, a Machine Learning Mastery ebook, and many more. This survey is also themed a medieval fantasy adventure - so you'll actually have some fun while participating!

In his regular courses at Georgia Tech, the computer science professor had at most a few dozen students. But his online class had 400 students -- students based all over the world; students who viewed his class videos at different times; students with questions. Maybe 10,000 questions over the course of a semester, Goel says. It was more than he and his small staff of teaching assistants could handle. "We were going nuts trying to answer all these questions," he says.