Hierarchical clustering is an important technique to organize big data for exploratory data analysis. However, existing one-size-fits-all hierarchical clustering methods often fail to meet the diverse needs of different users. To address this challenge, we present an interactive steering method to visually supervise constrained hierarchical clustering by utilizing both public knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven), and 2) enabling the interactive steering of clustering through a visual interface (user-driven). Our method first maps each data item to the most relevant items in a knowledge base. An initial constraint tree is then extracted using the ant colony optimization algorithm. The algorithm balances the tree width and depth and covers the data items with high confidence. Given the constraint tree, the data items are hierarchically clustered using evolutionary Bayesian rose tree. To clearly convey the hierarchical clustering results, an uncertainty-aware tree visualization has been developed to enable users to quickly locate the most uncertain sub-hierarchies and interactively improve them. The quantitative evaluation and case study demonstrate that the proposed approach facilitates the building of customized clustering trees in an efficient and effective manner.

Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-to-rank, decision forests deliver remarkable performance. They also possess other coveted characteristics such as interpretability. Despite their widespread use and rich history, decision forests to date fail to consume raw structured data such as text, or learn effective representations for them, a factor behind the success of deep neural networks in recent years. While there exist methods that construct smoothed decision forests to achieve representation learning, the resulting models are decision forests in name only: They are no longer axis-aligned, use stochastic decisions, or are not interpretable. Furthermore, none of the existing methods are appropriate for problems that require a Transfer Learning treatment. In this work, we present a novel but intuitive proposal to achieve representation learning for decision forests without imposing new restrictions or necessitating structural changes. Our model is simply a decision forest, possibly trained using any forest learning algorithm, atop a deep neural network. By approximating the gradients of the decision forest through input perturbation, a purely analytical procedure, the decision forest directs the neural network to learn or fine-tune representations. Our framework has the advantage that it is applicable to any arbitrary decision forest and that it allows the use of arbitrary deep neural networks for representation learning. We demonstrate the feasibility and effectiveness of our proposal through experiments on synthetic and benchmark classification datasets.

Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process is stationary. However, in many real-world applications, this assumption is violated, and using existing algorithms may result in a performance lag. To proactively search for a good future policy, we present a policy gradient algorithm that maximizes a forecast of future performance. This forecast is obtained by fitting a curve to the counter-factual estimates of policy performance over time, without explicitly modeling the underlying non-stationarity. The resulting algorithm amounts to a non-uniform reweighting of past data, and we observe that minimizing performance over some of the data from past episodes can be beneficial when searching for a policy that maximizes future performance. We show that our algorithm, called Prognosticator, is more robust to non-stationarity than two online adaptation techniques, on three simulated problems motivated by real-world applications.

We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a classifier. The guidance is provided by means of global explanations, which summarize the classifier's behavior on different regions of the instance space and expose its flaws. Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's quality. Moreover, XGL leverages global explanations to open up the black-box of human-initiated interaction, enabling supervisors to select informative examples that challenge the learned model. By drawing a link to interactive machine teaching, we show theoretically that global explanations are a viable approach for guiding supervisors. Our simulations show that explanatory guided learning avoids overselling the model's quality and performs comparably or better than machine- and human-initiated interactive learning strategies in terms of model quality.

We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete understanding of basic ethical knowledge. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.

Imagine that you are working on a project, with a team of 10 people. All members of this team, have to work from home now, because of the ongoing pandemic, so all of them have different laptops, different system specifications, different operating systems, etc. Now one fine day, a team member pushes a new change to GitHub, that adds some new functionality to your project. Unfortunately, these new changes do not work for some people, maybe because of different versions of the software installed on the different computers. So you have a very common problem, that many teams often face. "It works for him, but not for me" Docker was made specifically to solve this problem.

This "Python OOP: Object Oriented Programming in Python" course provides good understanding of object oriented concepts and implementation in Python programming. Design and development of a product requires great understanding of implementation language. The complexity of real world application requires the use of strength of language to provide robust, flexible and efficient solutions. Python provides the Object Oriented capability and lot of rich features to stand with changing demand of current world application requirement. This "Python OOP: Object Oriented Programming in Python" tutorial explains the Object Oriented features of Python programming in step-wise manner.

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Data Science is a gift to the modern world. The technology complements the existing data sources by making use of them. Recently, data science is being widely adopted by organizations to make predictive decisions on their behalf. Data science is a blend of various tools, algorithms and machine learning principles with the goal to discover hidden partners from raw data. The technology is primarily used to make decisions and predictions making use of predictive casual analytics, prescriptive analytics and machine learning.

You're not alone if you've been faced with this dilemma: The kids are now schooling from home and each need a computer to attend classes and get work done, yet budgets are tight because of a lost job or reduced hours because of the pandemic. To complicate matters further, parents may also be working from home this fall, and they, too, need a computer to get things done. And it may be your responsibility to pick one up in this B.Y.O.D. ("bring your own device") work world we're in today. Understandably, you might not be able to afford a fancy new computer for everyone in the home. If taking turns with a desktop or laptop isn't a viable option, the following are a few suggestions and workarounds that won't break the bank. If you don't need a lot of power or storage – which might be the case with many online "cloud" services used for school or work – perhaps buy a computer that runs on Google's ChromeOS, as it will cost a lot less, on average, than a Windows PC or Mac.