This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown course-level prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.

A former science teacher who believed in the power and possibility of online learning over two decades ago, he taught himself how to build courses in HTML on class intranets. Kevin taught one of the first hybrid, educational technology courses for teachers, for the University of Washington. And, after building countless web pages and classes on the early world wide web, he now helps develop e-learning programs, consults on virtual training'best practices' and has many interests in other internet and educational technology-related areas. Kevin finds he's now enjoying learning more from his children who are all deep into their own technology-related careers and entrepreneurial endeavors. With two new grandchildren, he's investigating more seriously the advancing new technologies in an effort to understand the knowledge and skills necessary to achieve happiness and success in a technological future.

Deep Learning is a shallow course that is akin to reading CliffsNotes instead of a textbook: you'll learn some terminology and be exposed to some interesting concepts but its abbreviated coverage is likely to confuse students who are new to neural networks while leaving more experienced students unsatisfied. This course seems like a rushed attempt to capitalize on the hottest buzzword in the hottest tech industry, which is a shame because it could have been a good course if it took the time to cover the topics in adequate detail. I give Deep Learning 2 out of 5 stars: Disappointing.

This seminar is part of the Oxford Martin School Hilary Term seminar series: Blurring the lines: the changing dynamics between man and machine Speakers: Dr Carl Frey, James Martin Fellow, Oxford Martin Programme on the Impacts of Future Technology Dr Michael Osborne, University Lecturer in Machine Learning, University of Oxford Will you one day lose your job to a robot, or even an algorithm? Dr Carl Frey and Dr Michael Osborne's recent working paper, 'The Future of Employment: How susceptible are jobs to computerisation?', found that nearly half of US jobs could be susceptible to computerisation over the next two decades. So as technology races ahead, will low-skilled workers need to retrain in order to remain part of the workforce?

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

In just our fourth session together, Steve was already beginning to sound discouraged. It was Thursday of the first week of an experiment that I had expected to last for two or three months, but from what Steve was telling me, it might not make much sense to go on. "There appears to be a limit for me somewhere around eight or nine digits," he told me, his words captured by the tape recorder that ran throughout each of our sessions. "With nine digits especially, it's very difficult to get regardless of what pattern I use--you know, my own kind of strategies. It really doesn't matter what I use--it seems very difficult to get." Steve, an undergraduate at Carnegie Mellon University, where I was teaching at the time, had been hired to come in several times a week and work on a simple task: memorizing strings of numbers. I would read him a series of digits at a rate of about one per second--"Seven ... four ... zero ... one ... one ... nine ..." and so on--and Steve would try to remember them all and repeat them back to me once I was done. One goal was simply to see how much Steve could improve with practice. Now, after four of the hour-long sessions, he could reliably recall seven-digit strings--the length of a local phone number--and he usually got the eight-digit strings right, but nine digits was hit or miss, and he had never managed to remember a 10-digit string at all. And at this point, given his frustrating experience over the first few sessions, he was pretty sure that he wasn't going to get any better. What Steve didn't know--but I did--was that pretty much all of psychological science at the time indicated that he was right. Decades of research had shown that there is a strict limit to the number of items that a person can retain in short-term memory, which is the type of memory the brain uses to hold on to small amounts of information for a brief period of time. If a friend gives you his address, it is your short-term memory that holds on to it just long enough to write it down. Or if you're multiplying a couple of two-digit numbers in your head, your short-term memory is where you keep track of all the intermediate pieces: "Let's see: 14 times 27 ... First, 4 times 7 is 28, so keep the 8 and carry the 2, then 4 times 2 is 8 ..." and so on.

We've put together an introductory lecture on TensorFlow as part of CS 224D, Stanford's deep-learning for NLP class. As far as we can tell, this is one of the first academic lectures on TensorFlow (aside from Google's official docs of course). We hope it'll prove useful to the ML community. Feel free to ask us questions on this thread, and we'll answer to the best of our ability.

Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression model, and now we're curious what is next. Right now, we can easily look at the data, and decide how "accurate" the regression line is to some degree. What happens, however, when your linear regression model is applied within 20 hierarchical layers in a neural network? Not only this, but your model works in steps, or windows, of say 100 data points at a time, within a dataset of 5 million datapoints.

Many people have gotten jobs in machine learning just by completing that MOOC. There're other similar online courses that help; for example the John Hopkins Data Science specialization. Participating in Kaggle or other online machine learning competitions has also helped people gain experience. Kaggle has a community with online discussions from which you can learn practical skills. Attending local meetups or academic conferences (if you can afford it) and talking to more experienced people will also help.

Cluster assessment is a staple of unsupervised machine learning and knowledge science. It is incredibly useful for knowledge mining and significant knowledge because it routinely finds patterns in the knowledge, without the will need for labels, contrary to supervised machine learning. In a true-world setting, you can think about that a robotic or an synthetic intelligence will not normally have access to the exceptional response, or it's possible there isn't an exceptional right response. You'd want that robotic to be able to investigate the world on its own, and study factors just by hunting for patterns. Do you ever ponder how we get the knowledge that we use in our supervised machine learning algorithms?