Education is one of the most powerful predictors of future success that human society has at its disposal. How we gather, process, and disseminate knowledge to each successive generation impacts not just individual success, but a host of other related factors such as economic growth, political empowerment, and technological innovation. It is no secret that access to more effective education for individual students is a key factor in the overall betterment of society – and to women's role in society. I've long been a proponent of better education for women – from my early career days working for CARE, to becoming the Chief Strategy Officer of Top Scholar, contributing to the book "Innovating Women" and to founding a non-profit to help the disadvantaged attain better education. Recently, I've been looking around globally for innovative solutions that can leapfrog women's education forward.

For many people, speaking off the cuff to a large audience does not come easily. But without professional feedback, rehearsing speeches and presentations can be a tough process. A psychologist, a management scientist and an IT specialist have developed an online training tool that uses artificial intelligence to evaluate users' speaking skills and personal characteristics. The team has now established the start-up Retorio at the Technical University of Munich (TUM) to launch the software on the market. It's a scenario many people can relate to – standing all alone in front of an audience, clutching a microphone with clammy hands and finding one's mouth has gone dry. Whether it's a job interview or a wedding speech: for many people, the idea of speaking in public is associated with anxiety and uncertainty.

Online education has become popular among working professionals and students. These categories of online learners find immense benefit in the autonomy, and flexibility, that these courses offer. Online courses can be planned into their schedule, which may include full-time employment, internships and caring for the family. It can also help them take out quiet time to study. The entire eLearning landscape around the globe is changing rapidly and new trends continue to emerge.

The first programming exercise "Twitter Sentiment Analysis in Python" is both fun and challenging, where you analyze tons of twitter message to find out the sentiments e.g.

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our previous algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the previous algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the previous algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

"With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of "collective intelligence" is coming more and more to the fore. The basic idea is that a group of individuals (e.g. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules." A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations.

Many young professionals, who have started their journey into data science, and machine learning, face a common problem -- they have completed one or two basic online course, done some programming lessons, put up a couple of projects on Github, and then… then what? In one of my previous articles on Medium (published by the TDS Team), I discussed, at length, where you can find MOOC (Massive Open Online Course) for jump-starting your journey into data science and machine learning. That article assumed the reader to be a beginner and covers essential MOOCs, which are optimized for basic and intermediate learning. I wrote another detailed article specifically focused on the topic of mathematics concepts you need to master for data science and machine learning and which courses to study. Recently, I have been receiving a lot of messages in my personal email and LinkedIn inbox, mostly from bright, young professionals, asking similar questions and my suggestions about online courses. I mostly have a ready answer for those messages.

We introduce the community exploration problem that has various real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from offline optimization to online learning. For the offline setting where the sizes of communities are known, we prove that the greedy methods for both of non-adaptive exploration and adaptive exploration are optimal. For the online setting where the sizes of communities are not known and need to be learned from the multi-round explorations, we propose an ``upper confidence'' like algorithm that achieves the logarithmic regret bounds. By combining the feedback from different rounds, we can achieve a constant regret bound.

We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric. These constraints demand that we select similar actions or individuals with approximately equal probability DHPRZ12, which may be at odds with optimizing reward, thus modeling settings where profit and social policy are in tension. We assume we learn about an unknown Mahalanobis similarity metric from only weak feedback that identifies fairness violations, but does not quantify their extent. This is intended to represent the interventions of a regulator who "knows unfairness when he sees it" but nevertheless cannot enunciate a quantitative fairness metric over individuals. Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on T, while obtaining an optimal O(sqrt(T)) regret bound to the best fair policy.

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs. However, most inverse optimization algorithms are designed specifically in batch setting, where all the data is available in advance. As a consequence, there has been rare use of these methods in an online setting suitable for real-time applications. In this paper, we propose a general framework for inverse optimization through online learning. Specifically, we develop an online learning algorithm that uses an implicit update rule which can handle noisy data. Moreover, under additional regularity assumptions in terms of the data and the model, we prove that our algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically consistent. In our experiments, we show the online learning approach can learn the parameters with great accuracy and is very robust to noises, and achieves a dramatic improvement in computational efficacy over the batch learning approach.