Catherine Stinson is a postdoctoral scholar at the Rotman Institute of Philosophy, at the University of Western Ontario, and former machine-learning researcher. I wrote my first lines of code in 1992, in a high school computer science class. When the words "Hello world" appeared in acid green on the tiny screen of a boxy Macintosh computer, I was hooked. I remember thinking with exhilaration, "This thing will do exactly what I tell it to do!" and, only half-ironically, "Finally, someone understands me!" For a kid in the throes of puberty, used to being told what to do by adults of dubious authority, it was freeing to interact with something that hung on my every word – and let me be completely in charge. For a lot of coders, the feeling of empowerment you get from knowing exactly how a thing works – and having complete control over it – is what attracts them to the job.
It included an invited talk, paper presentations, model AI assignments, a teaching and mentoring workshop, a best educational video award, and a robotics track. The symposium was established in response to growing community interest in sharing best practices for (1) how to teach AI and (2) how AI can serve as a motivating problem for teaching concepts in other areas of computer science, especially in introductory computer science courses. EAAI encourages the sharing of innovative educational approaches that convey or leverage AI and its many subfields, including robotics, machine learning, natural language, and computer vision. EAAI follows the successful 2008 Spring Symposium on "Using AI to Motivate Greater Participation in Computer Science" and the 2008 AAAI AI Education Colloquium. Fifty-five attendees formally registered for the event, but many other AAAI attendees also visited one or more EAAI events.
Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they're to understand their inherent limitations, and build even better platforms and intelligent systems -- we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level.
This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work.10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation). Computational thinking is often mistakenly equated with using computer technology. Algorithms are central to both computer science and computational thinking.
The following is a special contribution to this blog from Henry Kautz, Chair of the Department of Computer Science at the University of Rochester. His research interests are in knowledge representation, satisfiability testing, pervasive computing, and assistive technology. He is currently President of the Association for the Advancement of Artificial Intelligence (AAAI). If you have comments on this essay, e-mail Henry or add an entry to the bottom of this blog post. Countless gallons of ink (real and virtual) have been spilled on the need to infuse the humanities into science and engineering education.
On Friday, MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) hosted 150 local high school students for its second annual "Hour of Code" event, tied to the international initiative focused on getting kids interested in programming. Researchers showed off robots, 3-D-printing technology, and other projects to math and computer science students from schools throughout the greater Boston area, including Cambridge, Charlestown, Roxbury, and Somerville. The event also included a surprise video message from John Green, author of the bestselling young-adult novels-turned-movies "The Fault In Our Stars" and "Papertowns." Green commended the students on participating the event and elaborated on why coding is important. "I cannot emphasize enough how much I believe in learning computer science, not least because I am basically a first-grader when it comes to computer literacy," Green said.
The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining). CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. If you are not a Stanford student, you can still take CS246 as well as CS224W or earn a Stanford Mining Massive Datasets graduate certificate by completing a sequence of four Stanford Computer Science courses.
Reyes, Maritza (University of Texas at Austin) | Perez, Cynthia (Texas Tech University) | Upchurch, Rocky (New Deal High School, Lubbock, Texas) | Yuen, Timothy (University of Texas at San Antonio) | Zhang, Yuanlin (Texas Tech University)
This paper discusses the design of an introductory computer science course for high school students using declarative programming. Though not often taught at the K-12 level, declarative programming is a viable paradigm for teaching computer science due to its importance in artificial intelligence and in helping student explore and understand problem spaces. This paper describes the authors' implementation of a declarative programming course for high school students during a 4-week summer session.
Dubrovsky leads a bio-inspired robotics team at Harvard's Wyss Institute, and knows a lot about spark. He's pretty sure he's figured out a way of creating that spark, too: Root, an educational robot designed to teach kids--and adults--how to code. Zivthan Dubrovsky, Harvard Wyss Institute But the really neat part is the stacking of commands. Shay Pokress, a computer science curriculum developer and lead writer on the National Framework for K-12 Computer Science, got an early preview of Root and was impressed by its functionality.