The updates coincide with the annual National Robotics Week, a time when kids, parents and teachers across the nation tap into the excitement of robotics for STEM learning. Supporting Social and Emotional Learning The events of the past year changed the traditional learning environment with students, families and educators adapting to hybrid and remote classrooms. Conversations on the critical importance of diversity, equity and inclusion have also taken on increased importance in the classroom. To address this, iRobot Education has introduced social and emotional learning (SEL) lessons to its Learning Library that tie SEL competencies, like peer interaction and responsible decision-making, into coding and STEM curriculum. These SEL learning lessons, such as The Kind Playground, Seeing the Whole Picture and Navigating Conversations, provide educators with new resources that help students build emotional intelligence and become responsible global citizens, through a STEM lens. Language translations for iRobot Coding App More students can now enjoy the free iRobot Coding App with the introduction of Spanish, French, German, Czech and Japanese language support.
The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.
It's no secret that STEM professions--shaped by years of gender and racial bias--lack diversity. Machine learning engineering and research is no exception. Women currently hold around 25% of all computer science-related jobs, and only 12% of machine learning roles, with factors such as a lack of pay and career advancement transparency and a lack of women role models contributing to those numbers. But leaders in the machine learning and AI industry have in recent years woken to the value that women bring to the workforce. It doesn't just look good for a company to have diversity--it's integral to the success of organizations that build machine learning algorithms and artificial intelligence.
If you have finally decided to take the path from Excel-copy-and-paste to reproducible data science, then you will need to know the best route to take. The good news is that there is an abundance of free resources to get you there and awesome online communities to help you along the way. The bad news is that it can get overwhelming to pick which resources to take advantage of. This here is a no-nonsense guide that you can follow without regret, so you can spend less time worrying about the trail and more time trekking it. It's based on the lessons I learned when I went from a renewable energy project engineer who had never taken a statistics class to the head of a major data platform. At the trailhead for this journey, you can find an army of educated individuals doing data analysis by necessity, not passion.
As of March 23, get the full bundle for only $39.99. Computer science is always evolving. It's interlaced with all kinds of subjects in our tech-driven world, so more and more industries are requiring these skills -- from programming and business analysis to security and artificial intelligence. In fact, the number of job opportunities for computer science experts is growing faster than in any other occupation. If you're looking to expand the realm of opportunities available to you, it's not a bad idea to get familiar with the basics of computer science.
Deep learning is a powerful new technology, and we believe it should be applied across many disciplines. Domain experts are the most likely to find new applications of it, and we need more people from all backgrounds to get involved and start using it. That's why Jeremy cofounded fast.ai, to make deep learning easier to use through free online courses and software. Sylvain is a research engineer at Hugging Face. Previously he was a research scientist at fast.ai and a former mathematics and computer science teacher in a program that prepares students for entry into France's elite universities.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner A better way to build #machinelearning - why you should be using ActiveLearning? You spend less time and money on labelling data: Active learning has been shown to deliver large savings in data labelling across a wide range of tasks and data sets ranging from #computervision to #NLP. Since data labelling is one of the most expensive parts of training modern machine learning models this should be enough justification on its own. You get faster feedback on model performance: Usually people label their data before they train any models or get any feedback. Often, it takes days or weeks of iterating on #annotation guidelines and re-labelling only to discover that model performance falls far short of what is needed, or different labelled data is required.
Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.
A team of Carnegie Mellon University learning scientists are developing a tool that could change the way high school teachers and students approach their computer science classes. This month, Schmidt Futures announced that the team is one of the winners of their Futures Forum on Learning: Tools Competition. This award will fund tools to aid recovery from pandemic learning loss and advance the field of learning engineering. The tool, RecapCS, was created by Ember Liu and Neil Thawani with support from John Stamper, an assistant professor in the Human-Computer Interaction Institute. Liu and Thawani both graduated from the HCII's Master of Educational Technology and Applied Learning Science (METALS) program, which trains graduate students to become learning engineers and learning experience designers.
By Arnuld OnData, Industrial Software Developer turned Data Scientist. There is one thing you need to focus on first: how much math for data science. Learning Statistics was very confusing. I could not connect different parts of the topics. I took a STAT100 from Penn State online (a week), and I still could not remember anything.