An algorithm that extends an artificial-intelligence technique to new tasks could aid in analysis of flight delays and social networks.
Here is a great and simple explanation of a new network algorithm. I believe that another form of amplification of results that I seem to have seen concerns the overfitting problem. If I select a solution from a range and just chase after the highest or best criteria (of any sort: correlation, max error, r-squared, mean error, or more complex criteria) then the predictor equation seems to be over confident.
This clarified something I had confused for a while. I always thought that supervised machine learning was with humans in the loop, but according to this definition, it is just when there is a search on for an explanitory relationship to explain some y in terms of some array of x's. I guess the supervision can then also be the fitness criteria.
Think of learning analytics as the warning lights of online learning's dashboard – that gleaming assemblage of dials and icons that alerts a driver at once if gas in the tank is running low, a door is open, or the cooling system ...
It's great to have multiple projects to scan over and learn from. The one thing I would say is that learning analytics is a term that is being stretched to mean more than learning (and that is fine until some new name comes along). I think the best practice will be to talk about big data analytics for learning, retention, recruitment, alumni networks, business practices as all part of a new wave of using near real time analysis methods on big data sets to better understand and model learners.
It’s called Prismatic, and it may have solved one of the Internet’s oldest problems.......It's that good-and it has the potential to be far better still.Prismatic has no human editors. What you see on the page is governed entirely by machine-learning algorithms-that is, by software that adapts to you over time based on your interests and behaviour.The software's goal is to scour the entire Web for the stories most likely to interest, surprise, outrage, and delight you....Prismatic, on the other hand, navigates the shoals of predictability and incoherence with whimsy and grace.Use it for a few days, and you'll find yourself wondering, "How in the world did it know that I would be interested in that?"Use it for months, and you may suspect that the site knows you better than you know yourself....
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the ``last mile'' issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
Check out this beautiful infographic which answers why would colleges want to have analytics, how can educational data mining and learning analytics improve and personalize education, and how does this process work.
An infrographic to share with an educational data science team
How To Build A Successful Data Science Team InformationWeek The second data science role is that of machine-learning expert, a statistics-minded person who builds data models and makes sure the information they provide is accurate, easy to...
For a learning organization, I think we need to add a learning scientist and a games and simluations developer/researcher to make an all-around team.
Gamification is the use of game design concepts to create a layer on a real world setting. Typical gamification focuses on the use of rewards like points and badges to change the behavior of users, which can cause long-term damage to intrinsic motivation. Meaningful gamification is the use of design concepts from games and play to help people find personal connections to a real-world setting.
Conference theme: Intersection of learning analytics research, theory and practice The International Learning Analytics and Knowledge conference is now in its fourth year! LAK 14 will keep up the momentum generated in the ...