As a scientific field, the growth of mathematics education can be seen as a continuous process of having a deeper understanding of the complexity of learning and teaching. In order to enrich the curriculum and the teaching-learning process, most teachers are looking for new ways to engage students and each other. There are three ideas behind a revolution in education: diversity, curiosity, and innovation. Education needs to create a diverse curriculum that is accessible to inclusion where students are free to be innovative and awaken their imaginations and teachers encourage learning rather than pursuing compliance. A learning revolution is crucial for higher education, and we propose that thinking about complexity, with a focus on emergence, will empower educators to support substantive teaching-learning changes.

The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the first graduate-level, research-based artificial intelligence (AI) university in the world, has received immediate interest from graduate students across the globe. So far, 3,200 students have started the application process, including 1,681 potential students in the last step of their application process and 234 completing their applications within the first week of the university's launch on October 16. The majority of applications were received from the UAE, Saudi Arabia, Algeria, Egypt, India, and China, a statement said. Dr Sultan Ahmed Al Jaber, UAE Minister of State and chairman of the MBZUAI board of trustees, said: "The level of interest in such a short time is a very encouraging sign. MBZUAI is attracting prospective students from around the world, affirming the UAE leadership's vision of investing in human potential and enabling societies through knowledge and education to find practical solutions to some of the biggest challenges in the world, and further establishing the UAE and Abu Dhabi as a global hub for innovation and higher education."

So much of the conversation around artificial intelligence is about either the negative consequences, like job losses, or the dangers, in the form of A.I. bias or the potential misuse of facial recognition technology. As Axios's Kaveh Waddell reported this week, there can be a risk in trusting A.I. too much, for example in tasks like prescribing drugs or setting prison sentences. "These programs generally offer new information or a few options meant to help a human decision-maker choose more wisely," he wrote. "But an overworked or overly trusting person can fall into a rubber-stamping role, unquestioningly following algorithmic advice." And that points up the fault line with A.I. Instead of thinking of it as something we should or shouldn't trust -- which puts us in a passive mindset -- we should think of it as a tool we're in charge of, and one that will work to the extent that we direct it in the right way.

Every business has its top-level team, and the chief operating officer is one of the top-level managers at most companies. Following the path to become a COO is no small feat but joining the upper echelon of company management is certainly possible. Follow these tips, and you may find yourself with the COO title sooner than you'd think. Get a Solid Educational Base: The Bachelor's Degree Going to school to study business is a great way to start on the path toward becoming a COO, and the absolute minimum education suggested would be a bachelor's degree. The preferred areas of study are all business-related, of course, and the best choice for rounding out your business knowledge is a Bachelor of Business Administration degree.

This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize the exploration strategy to visit the maximum number of unique states in a limited number of steps. We particularly focus on environments with graph-structured state-spaces that are encountered in many important real-world applications like software testing and map building. We formulate this task as a reinforcement learning problem where the `exploration' agent is rewarded for transitioning to previously unseen environment states and employ a graph-structured memory to encode the agent's past trajectory. Experimental results demonstrate that our approach is extremely effective for exploration of spatial maps; and when applied on the challenging problems of coverage-guided software-testing of domain-specific programs and real-world mobile applications, it outperforms methods that have been hand-engineered by human experts.

-- This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public wellbeing. Our goal is to predict students who are at risk of high school dropout, by examining Louisiana administrative dataset. Due to the imbalanced nature of the dataset, imbalanced learning techniques including resampling, case weighting, and cost-sensitive learning have been applied to enhance the prediction performance on the rare class. Performance metrics used in this study are F-measure, recall and precision of the rare class. We compare the performance of several machine learning algorithms such as neural networks, decision trees and bagging trees in combination with the imbalanced learning approaches using an administrative dataset of size of 366k from Louisiana Department of Education. Experiments show that application of imbalanced learning methods produces good results on recall but decreases precision, whereas base classifiers without regard of imbalanced data handling gives better precision but poor recall. Overall application of imbalanced learning techniques is beneficial, yet more studies are desired to improve precision. Louisiana has maintained one of the highest school dropout rates in the US for many years. The Public Affairs Research Council of Louisiana (PAR, October 2011) estimates that one in six of every public high school students in the state drops out of school.

The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent's policy from overfitting to a limited set of training environments is to apply regularization techniques originally developed for supervised learning. However, there are stark differences between supervised learning and RL. We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL. In particular, we focus on regularization techniques relying on the injection of noise into the learned function, a family that includes some of the most widely used approaches such as Dropout and Batch Normalization. To adapt them to RL, we propose Selective Noise Injection (SNI), which maintains the regularizing effect the injected noise has, while mitigating the adverse effects it has on the gradient quality. Furthermore, we demonstrate that the Information Bottleneck (IB) is a particularly well suited regularization technique for RL as it is effective in the low-data regime encountered early on in training RL agents. Combining the IB with SNI, we significantly outperform current state of the art results, including on the recently proposed generalization benchmark Coinrun.

The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more severe. There were many researches about e-cigarette and cigarette that mostly focused on finding and analyzing causes of smoking using conventional statistics. However, there is a lack of research on developing prediction models, which is more applicable to anti-smoking campaign, about e-cigarette and cigarette. In this paper, we research the prediction models that can be used to predict an individual e-cigarette user's (including non-e-cigarette users) intention to smoke cigarettes, so that one can be early informed about the risk of going down the path of smoking cigarettes. To construct the prediction models, five machine learning (ML) algorithms are exploited and tested for their accuracy in predicting the intention to smoke cigarettes among never smokers using data from the 2018 National Youth Tobacco Survey (NYTS). In our investigation, the Gradient Boosting Classifier, one of the prediction models, shows the highest accuracy out of all the other models. Also, with the best prediction model, we made a public website that enables users to input information to predict their intentions of smoking cigarettes.

In the past decade, deep learning methods have seen tremendous success in various supervised and unsupervised learning tasks such as classification and generative modeling. More recently, deep neural networks have emerged in the domain of reinforcement learning as a tool to solve decision-making problems of unprecedented complexity, e.g., navigation problems or game-playing AI. Despite the successful combinations of ideas from quantum computing with machine learning methods, there have been relatively few attempts to design quantum algorithms that would enhance deep reinforcement learning. This is partly due to the fact that quantum enhancements of deep neural networks, in general, have not been as extensively investigated as other quantum machine learning methods. In contrast, projective simulation is a reinforcement learning model inspired by the stochastic evolution of physical systems that enables a quantum speed-up in decision making. In this paper, we develop a unifying framework that connects deep learning and projective simulation, opening the route to quantum improvements in deep reinforcement learning. Our approach is based on so-called generative energy-based models to design reinforcement learning methods with a computational advantage in solving complex and large-scale decision-making problems.

When introduced to machine learning, practically oriented textbooks and online courses focus on two major loss functions, the squared error for regression tasks and cross entropy for classification tasks, usually with no justification for why these two are important. Before we dive into why we might be interested in these loss functions, let's ensure that we're on the same page and quickly recall how they are defined. In Scikit-learn we find these as sklearn.metrics.mean_squared_error To explain why these two losses achieve what we want, we first need to agree on what exactly it is that we want to achieve. Let's consider a running regression example.