Serendipity35

Course Hero is a website where educators (and students) can find free resources. You need to create a free Verified Educator account which will allow you to access sample assignments, case studies, lectures, labs, syllabi, and more. These resources have been shared by higher education faculty and students. There are 80,000+ college faculty using the site to get learning resources and inspiration for your own teaching.

Your ability to download resources is gained by you uploading your original study materials. You’ll earn free unlocks for sharing your knowledge - 5 unlocks for every 10 documents submitted.

They are hosting their free 2-day 5th annual Education Summit July 28–29 from 9 a.m. – 4 p.m. PT. Their belief that teaching is a shared practice and the rapidly shifting educational landscape requires us to lean into our roles as both instructors and learners. You can join thousands of fellow educators, research experts, and instructional designers to unpack the latest in learning and pedagogy.

I first used their website when I was building a new course and was curious if any other teachers had uploaded their syllabi for a similar course. I found half a dozen that I was able to use to get started, including ones with links to readings I might also use.

Andrew Ng's Machine Learning course on Coursera has been revamped and updated and it is getting good student ratings.

There are fewer online courses that I consider to be true MOOCs now. Massive is small. Open is more closed. But the "OC" portion remains for many. The three courses that make up the Machine Learning Specialization offered by DeepLearning.AI and Stanford on the Coursera platform still fit the MOOC definition more closely.

You can earn a certificate at the end, and enjoy the full experiences including quizzes and assignments if you enroll and pay a monthly subscription but the courses are free (Open) to audit and view the course materials. The Massive in this course is massive with over 20,000 students enrolled.

Andrew Ng is the co-founder of Coursera and was the founding lead of Google’s Brain Project, and served as Chief Scientist at Baidu. He then did two artificial intelligence startups - Deeplearning.ai (a training company founded in 2017) and Landing.ai (for transforming enterprises with AI). He remains an adjunct professor at Stanford University. His course on Machine Learning was one of the very first courses from Coursera when it first launched in 2012. I audited the course that year though I knew the content was way above my abilitiees but I was curious as to the structure of the course from a design perspective.

At that time, Machine Learning was a new concept and was close to applied statistics. Ng goes way back because his Stanford lectures were on YouTube in 2008 and got 200,000 views. Then, he converted them to an online format in Fall 2011 and they were offered for free. He had 104,000 students and 13,000 of them gained certificates.

On the tech side, this updated version:
uses Python rather than Octave
expanded list of topics including modern deep learning algorithms, decision trees, and tools such as TensorFlow
new ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing
programming exercises
practical advice section on applying machine learning based on best practices from the last decade

 

On April 14, 2022, business magnate Elon Musk proposed to purchase social media company Twitter, Inc. for $43 billion. He had previously acquired 9.1 percent of the company's stock for $2.64 billion and thereby became its largest shareholder. Twitter invited Musk to join its board of directors and he accepted and then changed his mind. Musk is certainly one of the most unorthodox business leaders of our time. The general opinion seems to be that he would likely make changes to the platform that go well beyond revamping its content policies.

Twitter was generally not in favor of Musk taking control and so used what is known as a "poison pill" strategy. They would allow shareholders to purchase additional stock in the event a buyout should occur. But on April 25, Twitter's board of directors unanimously accepted Musk's buyout offer of $44 billion. There was also talk that Musk would make the company private.

Besides the business aspects of all this, many users were apprehensive about a Musk takeover and really about anyone taking over. The fear was not about stock prices or advertising. It is about how the platform would change.

Elon Musk published his first tweet on his personal Twitter account in June 2010. He had 80 million followers at the time of the purchase. Musk's most vocal comment about the purchase was that he wanted to protect "freedom of speech." Of course, that is something protected by the government and doesn't really apply to most private companies.

Elizabeth Lopatto of The Verge made some predictions about what a Musk takeover might mean. She thought that a mass employee exodus might occur. She also saw the reinstatement of some accounts, such as Donald Trump's account.

The New York Times wrote that Musk's acquisition was "about controlling a megaphone" rather than free speech. Kate Klonick, a law professor at St. John's University, went as far as to say that allowing "all free speech" would open the door to the spread of pornography and hate speech on Twitter.

A number of commenters have said that Musk's purchase just adds fuel to the controversy about the power that wealthy people have in influencing the democratic process.

Musk has said that he thought that Twitter should make the algorithm that determines what users see open-source and more transparent.

READ MORE
https://www.wsj.com/articles/twitter-under-elon-musk-what-an-open-source-and-free-speech-oriented-platform-could-look-like-11651091515