Beena Ammanath is the Founder and CEO of Humans For AI, a nonprofit organization focused on increasing diversity in tech leveraging AI. She is a recognized lead and industry expert who has driven pioneering technology changes in the use of AI, Data and Analytics for several market-leading companies. She has worked as a mentor to help women and minorities enter the new economy. She started Humans for AI in 2017 to help make AI understandable to the non-tech community. Beena is also an Industrial Board Member of Cal Poly University, where she brings the industry perspective to influence curriculum engineers.

Data Science is rapidly growing to occupy all the industries of the world today. Data Science has become very important in the Finance Industry, which is mostly used for Better Risk Management and Risk Analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 5 projects of Finance industry from scratch using real-world dataset, here's a sample of the projects we will be working on:

The reopening of schools in England is expected to take place in a "phased manner", says the Education Secretary Gavin Williamson. He told the Education Select Committee the date for opening would depend on scientific advice - but schools would get "as much notice as possible". But when pupils start returning it could just be for some year groups. "All schools returning on day one with a full complement of pupils would not be realistic," he told MPs. With schools closed by the coronavirus outbreak, the education secretary faced questions on a timetable for re-opening and how to support the disadvantaged, while pupils were meant to be learning online from home.

This means only one thing; you need to be prepared for constant learning. With all the abundance of abstract terms and an almost infinite number of details, the AI and ML learning curve can indeed be steep for many. But, getting started with anything new is hard, isn't it? Moreover, I believe everyone can learn it if only there is a strong desire. Besides, there is an effective approach that will facilitate your learning.

Embodied artificial intelligence (AI) tasks shift from tasks focusing on internet images to active settings involving embodied agents that perceive and act within 3D environments. In this paper, we investigate the target-driven visual navigation using deep reinforcement learning (DRL) in 3D indoor scenes, whose navigation task aims to train an agent that can intelligently make a series of decisions to arrive at a pre-specified target location from any possible starting positions only based on egocentric views. However, most navigation methods currently struggle against several challenging problems, such as data efficiency, automatic obstacle avoidance, and generalization. Generalization problem means that agent does not have the ability to transfer navigation skills learned from previous experience to unseen targets and scenes. To address these issues, we incorporate two designs into classic DRL framework: attention on 3D knowledge graph (KG) and target skill extension (TSE) module. On the one hand, our proposed method combines visual features and 3D spatial representations to learn navigation policy. On the other hand, TSE module is used to generate sub-targets which allow agent to learn from failures. Specifically, our 3D spatial relationships are encoded through recently popular graph convolutional network (GCN). Considering the real world settings, our work also considers open action and adds actionable targets into conventional navigation situations. Those more difficult settings are applied to test whether DRL agent really understand its task, navigating environment, and can carry out reasoning. Our experiments, performed in the AI2-THOR, show that our model outperforms the baselines in both SR and SPL metrics, and improves generalization ability across targets and scenes.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.

Katerina Fragkiadaki, an assistant professor in the School of Computer Science's Machine Learning Department, has received a National Science Foundation Faculty Early Career Development (CAREER) Award, the organization's most prestigious award for young faculty members. The five-year, $546,000 award will support her work on computer vision. Fragkiadaki's research interests include computer vision, robot behavior learning and visual language grounding. Her NSF-supported project will help her develop neural network architectures that take video inputs and not only learn to differentiate between camera motion and the scene, but also capture that scene and translate it into 3D maps. The agents are trained to predict the future rather than labels of objects and actions, greatly reducing the need for human supervision in learning.

Jessica Hammer, the Thomas and Lydia Moran Assistant Professor of Learning Science in the School of Computer Science's Human-Computer Interaction Institute, has received a National Science Foundation Faculty Early Career Development (CAREER) Award, the organization's most prestigious award for young faculty members. The $550,000 award will support her work on creating learning-supportive game-streaming interfaces. Hammer's proposed project will apply her research interests in games and learning theory to the game streaming website Twitch.tv. Many viewers already use Twitch to learn about everything from crafting to coding. To make the platform a more effective learning environment, Hammer will use learning theory to inform the design of a more interactive viewer interface and will create new educational games that take advantage of viewer participation.

The University of Oxford's Professor Rebecca Williams will deliver the closing keynote at this year's LegalEdCon, a virtual event, taking place on Thursday 14 May. Williams will use the slot to announce the findings of Oxford's'Unlocking the Potential of Artificial Intelligence for English Law' research project. She will focus in particular on the future of legal education in relation to changes to the legal job market resulting from implementation of lawtech, changes in the business models of law firms and developments in the law brought about by technology. Williams, along with fellow Oxford Uni akamdeics Ewart Keep and Václav Janeček, are responsible for the legal education stream of Oxford's UK Research and Innovation (UKRI)-funded AI for English Law project. The project as a whole brings together researchers from computer science, law, economics, education and the Saïd Business School to examine the potential and limitations of using AI in support of legal services.

As the university has shifted to virtual instruction in the wake of Covid-19, CS Professor Emeritus David Gries and Senior Lecturer Michael Clarkson '10 have transformed their approach to teaching CS 2110 Object-Oriented Programming and Data Structures. As an indication of their successful transition, a student in the class posted to Reddit "I appreciate that they listened to student feedback and changed things, and explained all of their decisions. That kind of communication and transparency should be a model for the rest of the school." So that others, beyond the classroom and in other classrooms, can benefit from a glimpse of their model, CS News inquired about what steps they have taken. In redesigning the course, we have emphasized learning and compassion over grades and logistics.