In his regular courses at Georgia Tech, the computer science professor had at most a few dozen students. But his online class had 400 students -- students based all over the world; students who viewed his class videos at different times; students with questions. Maybe 10,000 questions over the course of a semester, Goel says. It was more than he and his small staff of teaching assistants could handle. "We were going nuts trying to answer all these questions," he says.

The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3]. On the darker side, you do not have to be paranoid to be paranoid about the potential technologies that the super-secret computers of the US National Security Agency (NSA) have on their digital drawing-boards.

What are the most common pitfalls that new programmers face? Hesitance about putting in the time to learn about and utilize a good editor. Hesitance about putting in the time to learn about and utilize a good editor. As soon as I read this question I went online to see if I could find a great article I once read about things you should never say to a new programmer. I couldn't find it, but the main takeaway was that a new programmer should be given the time to do small programs using the language of their choice.

"How to Become a Super Learning Machine" is an excellent course that focuses on the practical basics of how to learn. The course teaches students about the right attitude to take when learning, the best way to absorb knowledge and how to set goals and achieve them. Thanks to my experience as a teacher, I went into the course understanding most of the concepts that Joe Parys covers. However, thanks to Joe's progressive and hybrid attitude towards learning I was able to take away some new things that have already helped me in my studies. First, Joe covers the importance of surrounding yourself with positive influence.

Techno-optimism is defined as the belief that technology can improve the lives of people. It was famously satired in the U.S. television comedy series "Silicon Valley," with a startup-company's founders pledging to "make the world a better place through Paxos algorithms for consensus protocols." But some people take techno-optimism very seriously. Ray Kurzweil, an accomplished tech innovator, described his techno-optimistic vision in his books: The Age of Spiritual Machines, How to Create a Mind: The Secret of Human Thought Revealed, and The Singularity Is Near. In a keynote address (see https://goo.gl/RwkwK1) at the 2016 meeting of the Computing Research Association, Kentaro Toyama argued that "In spite of the do-gooder rhetoric of Silicon Valley, it is no secret that computing technology in and of itself cannot solve systemic social problems."

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Dates: Self-paced (any time) Description excerpt: Do you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics! Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course. Advanced A few slightly more advanced topics covering optimization and applied linear algebra.

Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them. The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining). The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.