Headed to a university near you – Disruption. But not in the way you might expect. Most believe it will be the MOOCs (Massive Open Online Courses) that forever changes the landscape of higher education – but something much more close to all of us will be the demise. For years the college degree has been the path of success. The colleges said it was the path, and the culture followed suits pouring their hard earned (or borrowed) money into a college education for us and our children.

If the math behind data science is an enigma, the Khan Academy is a great place for insight. There are courses for different levels, and Sal Khan's relaxed delivery will get you through even the most difficult concepts (I think I have a small crush on him after the hours I've spent listening to his narratives!).

The term "artificial intelligence" has been around since 1956, and yet many journalists are unfamiliar with its history and impact on the world today, even as its influence grows everywhere, including on how we gather and report the news. The next massive open online course (MOOC) in Spanish, and the Knight Center's first in partnership with Microsoft, will familiarize students with the foundations of artificial intelligence (AI) and how it impacts the news industry. "Artificial Intelligence: How to cover AI and understand its impact on journalism," will run from Oct. 22 to Nov. 25, 2018 and will be taught by Sandra Crucianelli, a veteran instructor for Knight Center MOOCs and a member of the International Consortium of Investigative Journalists (ICIJ). "The course will be a wonderful opportunity for those who have not yet become familiar with artificial intelligence technologies," Crucianelli said. "We will be sharing definitions, but also analyzing applications, examples and there also will be online discussions. For example, will robots replace journalists? This is a question that many of us ask and I believe the exchange of opinions will be very interesting."

I leaned over the shoulder of a student in the library. She was quietly working with headphones in, and completely focused. What caught my attention is that she would continually lift her phone up over the textbook, and then jot something down on the paper to her left. It was a motion and process that she repeated at least seven times before I headed over to see what was going on. As I got closer I could see that it was a math textbook, and her paper was filled with equations, problems, and steps.

In recent years, there has been a lot of focus on adaptive e-learning, fueled by the advances of machine learning and artificial intelligence. As the one-size-fits-all approach of e-learning loses its appeal and online course attrition rates continue to rise, there is a move toward more personalized and adaptive learning to engage learners and achieve better learning outcomes. Personalized and adaptive learning has the ability to change learning content or the mode of delivery on the fly and to provide real-time feedback to learners. The origin of adaptive learning came from the research of intelligent tutoring systems, recommender systems and adaptive hypermedia. The advent of machine learning and artificial intelligence techniques have helped the plethora of platforms and tools that support adaptive learning flourish.

Those who learn how to make machines that exhibit intelligence today are tomorrow going to lead the next technological revolution, be part of the most cutting-edge companies and stand a chance to disrupt almost all industries through their skillsets.

The terms'data science' and'analytics' have had a great surge in usage for a long time now. Data has become the driving force of all established companies these days. In this ever-evolving sector of technology, success and failure stories are found daily. But only a few of them are as inspiring as the story of Ankush Bhandari, a tiffin service provider who became a data scientist. Ankush Bhandari completed his Master in Economics at Fergusson College, Pune, and started his entrepreneurial venture – Kaivalya Foods – as a tiffin service provider.

Einstein once said that if you judge a fish by its ability to climb a tree, it'll live its whole life thinking it's stupid. What you may not know is that he was referring to the public educational system and the one size fits all approach to teaching. Although more than a century passed since he said this, traditional models of education still exist till this day and still insist on standardized teaching techniques, despite their inability to deliver the best results. More and more learners -- in the academic and business world -- don't find these models challenging and engaging, so they're searching for alternatives, they want more personalized learning experiences. Personalized learning is the tailoring of learning environments with the primary focus on learners and how they experience the process of knowledge acquisition.

A few months ago I had the opportunity to complete Andrew Ng's Machine Learning MOOC taught on Coursera. It serves as a very good introduction for anyone who wants to venture into the world of AI/ML. I always wondered how amazing this course could be if it were in Python. I finally decided to re-take the course but only this time I would be completing the programming assignments in Python. In these series of blog posts, I plan to write about the Python version of the programming exercises used in the course.

Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Therefore, Kalman filter may be approximated by regression on a few recent observations. Surprisingly, we also show that having some process noise is essential for the exponential decay. With no process noise, it may happen that the forecast depends on all of the past uniformly, which makes forecasting more difficult. Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations. We use our decay results to provide the first regret bounds w.r.t. to Kalman filters within learning an LDS. That is, we compare the results of our algorithm to the best, in hindsight, Kalman filter for a given signal. Also, the algorithm is practical: its per-update run-time is linear in the regression depth.