In this article, ten questions about MOOCs (crowdsourced from the recipients of the AAAI and SIGCSE mailing lists) were posed by editors Michael Wollowski, Todd Neller, James Boerkoel to Douglas H. Fisher, Charles Isbell Jr., and Michael Littman — educators with unique, relevant experiences to lend their perspective on those issues.

In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.

Additional landmark events in the past 20 or so years that looked at the challenges of AI education have included the AI Education Workshop held at the 2008 AAAI conference and the Improving Instruction of Introductory Artificial Intelligence symposium held at the 1994 AAAI Fall Symposium. To quote Marti Hearst, the organizer of the 1994 symposium (Hearst 1994): "This symposium was motivated by the desire to address an oft-voiced complaint that introductory artificial intelligence is a notoriously difficult course to teach well." With the regular progression of the field and recent successes such as autonomous cars, deep learning, and IBM's Watson system, this situation has not become easier. At the same time, recent innovations in pedagogical technologies, such as massive open online courses (MOOCs), smartphones, and smart classrooms, have revolutionized how we view the art of teaching. We believe that now is a good time to take stock of state-of-the-art practices in the teaching of AI, as well as propose a vision for AI education in the future. This issue of AI Magazine includes five articles at the cutting edge of AI education. Each covers a subject of current concern to the AI education community. We note that the subject area expertise of the authors covers a wide range including robotics, knowledge-based systems, ethics, machine learning, and game theory. The article Ask Me Anything About MOOCs by Douglas Fisher, Charles Isbell, and Michael Littman was a unique project.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

I have a background in Computer Science and worked with nearly every programming language on the planet. I graduated with highest distinction during my masters program. I've worked on projects ranging from Robotics, Web Apps, Mobile Apps to Embedded Systems. These courses will help you achieve your goals.

For those who are looking for something a little less costly, Udacity also offers a number of free machine learning courses ranging from 10 weeks to four months. Lynda from LinkedIn is a leading online learning platform that helps anyone learn a wide range of skills, including machine learning. To help grasp the basics of technology and data mining, Alison is a Galway-based e-learning platform offering a number of free online courses on software development, data science and machine learning. Udemy offers thousands of online courses with which to upskill, including a number of machine learning courses.

AI promises to transform the world. Companies like this one will pave the way. He's been called one of the "foremost thinkers on the topic of artificial intelligence," so it's no surprise that Andrew Ng -- the cofounder of Coursera, the lead developer of Stanford University's main Massive Open Online Course (MOOC) platform, and the founder of the Google Brain project -- is starting another AI company of his own now that he's left Baidu. The resume of this impressive entrepreneur reads like a laundry list of some of the most impressive achievements in AI technology, and it seems safe to assume that Ng's newest venture, known only as deeplearning.ai, Hope will help many of you: deeplearning.ai

According to Google Trends, machine learning has shown a steady (almost threefold) increase in interest since 2015. Coursera and Udacity machine learning courses are both in the top ten related topics. It appears that many people want to learn more about it. If you have ever used Google, Netflix, Amazon, Gmail, then you have interacted with machine learning (ML). It has become an important component in online retail, recommendation systems, fraud detection and others.

Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the $T$-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.