The fastest way to get good at applied machine learning is to practice on end-to-end projects. In this post you will discover how to work through a regression problem in Weka, end-to-end. Step-By-Step Regression Machine Learning Project Tutorial in Weka Photo by vagawi, some rights reserved. This tutorial will walk you through the key steps required to complete a machine learning project in Weka. Weka is the best platform for beginners getting started in applied machine learning.

Microsoft India today announced the winners of the 24 hour hackathon, held as part of the first Machine Learning & Data Sciences (ML&DS) Conference. Team Builders won the grand prize, while Eavesdroppers and NHacks emerged as the first and second runners up respectively by creating applications to address challenges in the field of agriculture, call center management and human behavior. The hackathon invited students and early-stage developers to work in teams to build intelligent, working applications with the help of Microsoft Cognitive Services, Microsoft Bot Framework and Microsoft R Services. The top three teams won cash prizes worth INR 50,000, INR 40,000 and INR 30,000 for developing solutions fulfilling parameters on design, innovation, foreseeable impact and marketability. In addition to the cash awards, the student participants from the winning teams will also be offered interviews for internships and full time positions at Microsoft India (R&D) Pvt. Ltd. "Microsoft has the vision, strategy and talent to democratize data and machine learning and use it to realize our mission to empower every individual and organization on the planet to achieve more. The hackathon was designed to spark curiosity among diverse talent to develop applications using our services and demonstrate the versatility and potential of using the platform. It has also shown us how Microsoft's APIs could help scale ideas with global relevance in a very short time," said Anil Bhansali, Managing Director, Microsoft India (R&D) Pvt. Ltd.

You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. One of the key themes for the event emerged from a survey of the attendees. Everybody who responded to the survey said that ML is either important or very important to their company and industry. However, more than half of the respondents said their organizations did not have adequate expertise in ML to be able to do what they need to do.

Today, The Washington Post posted a new in-depth interview with Tim Cook, where he discusses his first five years as Apple CEO, the company's future, artificial intelligence, virtual reality, augmented reality, and much more. The interview, which includes not only text but also video of Cook talking over a variety of topics, is lengthy and covers a lot of content. Cook talked about services and how important they are to Apple's present and future: "Its services business, which includes things like iTunes, iCloud and a mobile payments service, is projected to be the size of a Fortune 100 business next year -- all on its own." Cook also discussed some of the mistakes that Apple has made in the past, including the hire of John Browett, who ran Apple Retail stores for a short period of time. Today we have a product we're proud of.

The past years have witnessed strong emergence for different datasets and algorithms repositories. Some inquiries accompanied this emergence. An increasing amount of market research started to investigate which is more important for the development of Artificial Intelligence (AI) sciences, which segments are of highest demand and can have greater market share in the future. By reviewing the artificial intelligence (AI) breakthroughs timeline over 30 years, Wissner-Gross found that the availability of high-quality datasets was the key limiting factor for AI advances and not algorithms. He also found that high-quality dataset availability can cause a breakthrough in the field of AI six times faster than Algorithms.

The dream of one day having an entity with artificial intelligence diagnose your condition and recommend the best treatment may still be years away, but at IBM Watson Health and elsewhere, the technology and capability is evolving at such a rapid pace that such a function could well be part of regular healthcare practices. About a month ago I interviewed Deborah DiSanzo, who is IBM's General Manager for Watson Health. She was previously the CEO of Phillips Healthcare but now spearheads the development of Watson Health into a multi-billion-dollar business unit for IBM. "I was at one of our larger partners who is actually using our application from IBM called Clinical Trial Matching, which enables oncologists to, from the hundreds of thousands of clinical trials that are going on, match the appropriate clinical trial to the patient. And the breast oncologist that I was speaking to said it is fantastic because it "enables me to speak to my patients better, I turn the screen around and I show her what the particular type of breast cancer she has, how that matches with the top three clinical trials that she could go on.""

Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps.

The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of being inherently parallelizable. However, the standard choice of using a fixed number of particles at each iteration is suboptimal because some steps will contribute disproportionately to the variance of the estimate. We introduce an adaptive version of the Resample-Move algorithm, in which the particle set is adaptively expanded whenever a better approximation of an intermediate distribution is needed. The algorithm builds on the expression for the optimal number of particles and the corresponding minimum variance found under ideal conditions. Benchmark results on challenging Gaussian Process Classification and Restricted Boltzmann Machine applications show that Adaptive Resample-Move (ARM) estimates the normalizing constant with a smaller variance, using less computational resources, than either Resample-Move with a fixed number of particles or Annealed Importance Sampling. A further advantage over Annealed Importance Sampling is that ARM is easier to tune.

We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is naturally constrained to refine single-linkage clusters and be refined by maximal-linkage clusters. We work in the context of metric spaces with non-expansive maps, which is appropriate for modeling data processing which does not increase information content.

In this paper, the online variants of the classical Frank-Wolfe algorithm are considered. We consider minimizing the regret with a stochastic cost. The online algorithms only require simple iterative updates and a non-adaptive step size rule, in contrast to the hybrid schemes commonly considered in the literature. Several new results are derived for convex and non-convex losses. With a strongly convex stochastic cost and when the optimal solution lies in the interior of the constraint set or the constraint set is a polytope, the regret bound and anytime optimality are shown to be ${\cal O}( \log^3 T / T )$ and ${\cal O}( \log^2 T / T)$, respectively, where $T$ is the number of rounds played. These results are based on an improved analysis on the stochastic Frank-Wolfe algorithms. Moreover, the online algorithms are shown to converge even when the loss is non-convex, i.e., the algorithms find a stationary point to the time-varying/stochastic loss at a rate of ${\cal O}(\sqrt{1/T})$. Numerical experiments on realistic data sets are presented to support our theoretical claims.