MOOCs have been around since 2008, when 25 students attended a course on connectivism at the University of Manitoba - with 2,300 joining online worldwide. They really hit the public consciousness around 2012, when Coursera was created. It partnered with universities to offer courses online, typically with a mix of active participation and self-paced study using filmed lectures and reading lists. Its closest competitor, edX, is a joint venture of Harvard and MIT. As the name suggests, MOOCs are designed for unlimited participation and are free, subsidized, or much cheaper than traditional higher education courses. Although they have been criticized for their low completion rate - an MIT study found an average quit rate of 96% over five years - that has done nothing to stop the demand. In 2019, MOOCs had attracted 110 million students and more than 900 universities around the world had submitted 13,500 courses.

Description To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as NumPy, TensorFlow and PyTorch, to solve whichever problem you have at hand. To be an excellent data scientist, you need to know how those libraries and algorithms work. This is where our course "Machine Learning & Data Science Foundations Masterclass" comes in. Led by deep learning guru Dr. Jon Krohn, this first entry in the Machine Learning Foundations series will give you the basics of the mathematics such as linear algebra, matrices and tensor manipulation, that operate behind the most important Python libraries and machine learning and data science algorithms. Throughout each of the sections, you'll find plenty of hands-on assignments and practical exercises to get your math game up to speed!

The Ultimate Bundle to learn Machine Learning which consists of online courses that will teach you about ML models and vectors using R and Python. A mighty Bundle designed to meet all your learning need for Artificial Intelligence & Machine Learning. It is power-packed with 20 Courses having 110 hrs of video that will teach you AI & ML from scratch. With this bundle, you will not only learn basic AI & ML but also programming with Python & R, usage of different AI & Data Science libraries, training machine learning algorithms, building various models, deep learning and so much more. Not only this, but we have also included a course dedicated entirely to mathematical foundations behind machine learning!

You may not need to ask teachers how your kid is performing in studies as his or her tweets will be enough to gauge whether he or she will make it big in the future or not, thanks to Artificial Intelligence (AI). A team of Russian researchers has used AI-based models to predict high academic achievers from lower ones based on their social media posts. The prediction model uses a mathematical textual analysis that registers users' vocabulary (its range and the semantic fields from which concepts are taken), characters and symbols, post length and word length. Every word has its own rating (a kind of IQ). Scientific and cultural topics, English words, and words and posts that are longer in length rank highly and serve as indicators of good academic performance.

Offered by Duke University. In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

Identifying whole/part relationships between books in digital libraries can be a valuable tool for better understanding and cataloging the works found in bibliographic collections, irrespective of the form in which they were printed. However, this relationship is difficult to learn computationally because of limited ground truth availability. In this paper, we present an approach for data augmentation of whole/part training data through the use of artificially generated books. Artificial data is found to be a robust approach to training deep neural network classifiers on books with limited real ground truth, working to prevent over-fitting and improving classification by 91.0%. Modern cataloging standards support encoding complex work-level relationships, opening the possibility for bibliographic collections that better represent the complex ways that works are changed, iterated, and collated in library books.

We study the problem of online learning with primary and secondary losses. For example, a recruiter making decisions of which job applicants to hire might weigh false positives and false negatives equally (the primary loss) but the applicants might weigh false negatives much higher (the secondary loss). We consider the following question: Can we combine "expert advice" to achieve low regret with respect to the primary loss, while at the same time performing {\em not much worse than the worst expert} with respect to the secondary loss? Unfortunately, we show that this goal is unachievable without any bounded variance assumption on the secondary loss. More generally, we consider the goal of minimizing the regret with respect to the primary loss and bounding the secondary loss by a linear threshold. On the positive side, we show that running any switching-limited algorithm can achieve this goal if all experts satisfy the assumption that the secondary loss does not exceed the linear threshold by $o(T)$ for any time interval. If not all experts satisfy this assumption, our algorithms can achieve this goal given access to some external oracles which determine when to deactivate and reactivate experts.

Abstract--We present a simple generative model in which spectral graph embedding for subsequent inference succeeds whereas unsupervised graph convolutional networks (GCN) fail. The geometrical insight is that the GCN is unable to look beyond the first non-informative spectral dimension. 's, we observe a Euclidean space and Email: shenc@udel.edu - Ningyuan (Teresa) Huang and Tianyi Chen are with the Department of's are corrupted through the Bernoulli noise The authors thank Wade Shen for providing the motivation for this investigation. Geometry for the canonical case where ASE succeeds but GCN fails. Figure 1 illustrates the failure mode for GCN.

We analyze statistical discrimination using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez-faire may result in a highly unfair and inefficient outcome---myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated---they are never hired, and therefore, data about them is never accumulated. We proved that this problem becomes more serious when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is the Rooney Rule, which requires firms to interview at least one minority worker for each hiring opportunity. Our results indicate temporary affirmative actions are effective for statistical discrimination caused by data insufficiency.

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for our approach, which is no worse asymptotically than standard RL, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Empirically, we show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates during training than prior methods. Videos are at this url https://sites.google.com/view/ Reinforcement learning (RL) is a powerful framework for learning-based control because it can enable agents to learn to make decisions automatically through trial and error. However, in the real world, the cost of those trials - and those errors - can be quite high: an aerial robot that attempts to fly at high speed might initially crash, and then be unable to attempt further trials due to extensive physical damage. However, learning complex skills without any failures at all is likely impossible.