There are places in the tech space where we cease to stare in amazement about what the tech can do. Instead we whine that the tech can't do more. Take the case of handwriting recognition, whether it's what we scribble notes onto a tablet or when we scan handwritten text into a PC. We wish that it was smarter, that it recognized more characters and that the text was searchable and shareable. To be honest, I shouldn't say "we".

The 11th WiML Workshop is co-located with NIPS in Barcelona, Spain on Monday, December 05, 2016. The workshop is a full-day event with invited speakers, oral presentations, and posters. The event brings together female faculty, graduate students, and research scientists for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. There will also be a panel discussion, a mentoring session and a pre-workshop dinner to discuss current research trends and career choices in machine learning.

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification is that it is simply an instance of online gradient descent (OGD), during mistake rounds, using the hinge loss function. Motivated by this interpretation, we propose a novel family of listwise, large margin ranking surrogates. Members of this family can be thought of as analogs of the hinge loss. Exploiting a certain self-bounding property of the proposed family, we provide a guarantee on the cumulative NDCG (or AP) induced loss incurred by our perceptron-like algorithm. We show that, if there exists a perfect oracle ranker which can correctly rank each instance in an online sequence of ranking data, with some margin, the cumulative loss of perceptron algorithm on that sequence is bounded by a constant, irrespective of the length of the sequence. This result is reminiscent of Novikoff's convergence theorem for the classification perceptron. Moreover, we prove a lower bound on the cumulative loss achievable by any deterministic algorithm, under the assumption of existence of perfect oracle ranker. The lower bound shows that our perceptron bound is not tight, and we propose another, \emph{purely online}, algorithm which achieves the lower bound. We provide empirical results on simulated and large commercial datasets to corroborate our theoretical results.

With the use of education chatbots, Prepathon CEO Allwin Agnel explained that the artificial intelligence-driven education technology bots are able to execute precise and detailed tasks that can improve or alter educational experiences by facilitating personalized learning. As the equity gap in American education continues, Microsoft co-founder Bill Gates has been urging educators, investors and tech companies to be more open in investing time and money in artificial intelligence-driven education technology programs. Gates believed that these AI-based EdTech platforms could personalize and revolutionize school learning experience while eliminating the equity gap. With that said, Gates is reportedly excited about the evolving field of personalized learning and artificial intelligence tutor bots. According to Venture Beat, the world's richest man will also like the Mumbai-based company called Prepathon as it opted to create bots with specialized single concentration and purpose.

This is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. To officially register for this course you must create a profile in our web app. Just create an account on GitHub and log in with this account in our web app. The intention of this app is to offer for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc.

Faculty Opening Young and research-intensive, Nanyang Technological University (NTU Singapore) is ranked 13th globally. It is also placed 1st amongst the world's best young universities and ranked 6th globally for Engineering and Technology. With an engineering research output ranked amongst the top seven universities in the world, the College of Engineering (CoE) is a recognised leader in innovation and technology. CoE is the largest College in NTU and comprises six internationally known engineering schools with more than 14,000 students, 550 faculty and 1,400 staff. At the College of Engineering, faculty are encouraged to seek new knowledge, discover and create innovative solutions.

Hadoop distributor MapR has raised a new round of funding and may be preparing for an IPO next year, Salesforce acquires analytics startup BeyondCore, Coursera releases a new data analytics course together with PwC, and Apple CEO Tim Cook provided some illumination on how his company regards artificial intelligence (AI). We've got all the highlights in this Big Data Roundup for the week ending Aug. 21, 2016. Let's start with the news from Hadoop distributor MapR. The company recently announced that it has raised a round of equity financing worth 50 million, and provided a few select details about its financial performance. MapR is still a privately held company, so it can choose what to disclose and what not to disclose.

New ranking algorithms are continually being developed and refined, necessitating the development of efficient methods for evaluating these rankers. Online ranker evaluation focuses on the challenge of efficiently determining, from implicit user feedback, which ranker out of a finite set of rankers is the best. Online ranker evaluation can be modeled by dueling ban- dits, a mathematical model for online learning under limited feedback from pairwise comparisons. Comparisons of pairs of rankers is performed by interleaving their result sets and examining which documents users click on. The dueling bandits model addresses the key issue of which pair of rankers to compare at each iteration, thereby providing a solution to the exploration-exploitation trade-off. Recently, methods for simultaneously comparing more than two rankers have been developed. However, the question of which rankers to compare at each iteration was left open. We address this question by proposing a generalization of the dueling bandits model that uses simultaneous comparisons of an unrestricted number of rankers. We evaluate our algorithm on synthetic data and several standard large-scale online ranker evaluation datasets. Our experimental results show that the algorithm yields orders of magnitude improvement in performance compared to stateof- the-art dueling bandit algorithms.

Data Science is not just about data. The bare basics are recognizing what all data to keep, identifying how to process it for different results. It does not stop there. Data scientists need to figure out blanks in data and fill them with data that'may' come up in future. Data Science essentially is about connecting dots in businesses and using existing and non-existing data to meet the demands of each business.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.