The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.

If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way.

Get the social sector's most essential news coverage, including news highlights, opinion pieces and features to keep you up-to-date with Australia's most valuable sector. Get news covering the latest innovations in local and international for-good business practices. Get purpose-driven roles delivered straight to your inbox. Accompanied by the latest careers news, including Changemakers and who's moving where in the sector, you'll never be out of touch with career developments within the sector. Get notifications on the latest webinar topics, as well as other Pro Bono Australia professional development resources.

So you want to learn Data Science with R? Good Decision! Because R programming has various statistical and graphical capabilities. R has a huge variety of libraries to perform statistical analysis. Some most powerful visualization packages in R are ggplot2, ggvis, googleVis, and rCharts. So if you are looking for the Best Online Courses for Data Science with R, then this article will help you.

Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo

Starting a business can be costly, especially in certain fields such as brick-and-mortar and retail. But there are ways to drastically reduce your startup costs, and to secure funding without giving away the rights to your company, or going into serious debt. In this course, you are going to learn about some of the best ways to save money, get profitable faster, and avoid having to seek funding before your company is truly ready. You're going to learn how to start your business with the least possible investment, and how to manage your money until your company becomes profitable. This course is your fast track to startup success and will provide long-lasting value from your very first business you start right through to advanced enterprise campaigns.

PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient. PyCaret is simple and easy to use.

We have a serious problem with how we have been teaching computability theory, a central component of the ACM/IEEE computer science curriculum. For a fair number of years, I taught a computability course.

This interactive workshop introduces the principles and practices of machine learning using the Python programming language and its associated …

Description Unsupervised Machine Learning involves finding patterns in datasets. After taking this course, students will be able to understand, implement in Python, and apply algorithms of Unsupervised Machine Learning to real-world datasets. The core of this course involves detailed study of the following algorithms: Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model Dimension Reduction: Principal Component Analysis The course presents the math underlying these algorithms including normal distributions, expectation maximization, and singular value decomposition. The course also presents detailed explanation of code design and implementation in Python, including use of vectorization for speed up, and metrics for measuring quality of clustering and dimension reduction. The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).