The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approaches that are used in the fields of perception, planning and control as well as end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to a range of high performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms emphasizing both the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field we conclude with a summary of open research challenges that will guide future researchers in this field.

This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge the gap between high school maths and deep learning. As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University…, I see more students who are familiar with programming than with mathematics. They have last learnt maths years ago at University. And then, suddenly they find that they encounter matrices, linear algebra etc when they start learning Data Science.

Are you looking for the Best Advanced Machine Learning Courses?… If yes, then this article is for you. In this article, you will find the 10 Best Advanced Machine Learning Courses. To gain Machine Learning skills, there are numerous courses available. So, without wasting your time, let's start finding the Best Advanced Machine Learning Courses– This is a Nanodegree Program offered by Udacity.

Deeper learning is an internal process and ultimately individual, so we need to strengthen our self-learning skills to move forward in this process as deeper and lifelong learners. For more information, refer to articles, I have written before in Medium. First, set aside three big mistakes in self-study: 1. Do not think that learning will end one day 2. Set aside unplanned learning. Second, remember three big tips: 1. Define your learning structure: Goals, Resources, and Time to reach the goals. Curiosity makes our brains more receptive to learning, according to research at the University of California.

One of the most common questions that data science aspirants have is "how much math do I need to know for machine learning?" Students looking to break into machine learning often see math as a huge barrier to entry. Gatekeepers in the industry don't help with this concern, labelling students as unqualified unless they have a Master's degree of PhD in the subject. So how much math do you need to know in order to work in the data science industry? The answer: Not as much as you think.

Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career.

Data Science is a multi-disciplinary field that capitalizes on theories, methods, techniques, and algorithms from information technology (ICT), including visualization and machine learning. Data Science builds upon a foundation of theories from computer science, mathematics (in particular statistics), and social sciences, etc., to enable a large variety of applications, including predictive analytics and business intelligence, data-driven sciences (big data science), and artificial intelligence. Data Science and Artificial Intelligence are two tightly related fields, sharing major research challenges and resulting technologies, with machine learning as a notably prominent example. Teaching computers human-like, intelligent behavior is increasingly often achieved by letting AI systems learn from large and rich data, critically depending on know-how and skills from data science. About CEDAS and Data Science: In order to coordinate, strengthen, and prioritize local research efforts in data science, we are operating and extending a new research center for data science, CEDAS, together with a growing number of partners.

It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as a MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and development of persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models shows 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.

Ethical concerns regarding Artificial Intelligence (AI) technology have fueled discussions around the ethics training received by AI designers. We claim that training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology and development, and pedagogy, we propose a functional way to provide this training. This article appears in the special track on AI & Society.

The phenomenon of benign overfitting, where a predictor perfectly fits noisy training data while attaining low expected loss, has received much attention in recent years, but still remains not fully understood beyond simple linear regression setups. In this paper, we show that for regression, benign overfitting is ``biased'' towards certain types of problems, in the sense that its existence on one learning problem precludes its existence on other learning problems. On the negative side, we use this to argue that one should not expect benign overfitting to occur in general, for several natural extensions of the plain linear regression problems studied so far. We then turn to classification problems, and show that the situation there is much more favorable. Specifically, we consider a model where an arbitrary input distribution of some fixed dimension $k$ is concatenated with a high-dimensional distribution, and prove that the max-margin predictor (to which gradient-based methods are known to converge in direction) is asymptotically biased towards minimizing the expected \emph{squared hinge loss} w.r.t. the $k$-dimensional distribution. This allows us to reduce the question of benign overfitting in classification to the simpler question of whether this loss is a good surrogate for the misclassification error, and use it to show benign overfitting in some new settings.