Within the realm of service robotics, researchers have placed a great amount of effort into learning motions and manipulations for task execution by robots. The task of robot learning is very broad, as it involves many tasks such as object detection, action recognition, motion planning, localization, knowledge representation and retrieval, and the intertwining of computer vision and machine learning techniques. In this paper, we focus on how knowledge can be gathered, represented, and reproduced to solve problems as done by researchers in the past decades. We discuss the problems which have existed in robot learning and the solutions, technologies or developments (if any) which have contributed to solving them. Specifically, we look at three broad categories involved in task representation and retrieval for robotics: 1) activity recognition from demonstrations, 2) scene understanding and interpretation, and 3) task representation in robotics – datasets and networks. Within each section, we discuss major breakthroughs and how their methods address present issues in robot learning and manipulation. A Survey of Knowledge Representation and Retrieval for Learning in Service Robotics

A cheezy Le Monde mathematical puzzle : (which took me much longer to find [in the sense of locating] than to solve, as Warwick U does not get a daily delivery of the newspaper [and this is pre-Brexit!]):

Creating and plotting decision trees (like one below) for the models created in H2O will be main objective of this post: || |Figure 1. Decision Tree Visualization in R|

Linear Regression Model Diagnostics for Survey Data (svydiags)Contains functions for computing diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to …

How would I miss to program just a little bit during the holiday season? But I didn’t want to work on something serious, so I decided to checkout some ground work on R-Shiny + JQuery + CSS. The result are some nice holiday greetings inside a shiny app:

The constant evolution of technology has meant data and information is being generated at a rate unlike ever before, and it’s only on the rise. Furthermore, the demand for people skilled in analyzing, interpreting and using this data is already high and is set to grow exponentially over the coming years. These new roles cover all aspect from strategy, operations to governance. Hence, the current and future demand will require more data scientists, data engineers, data strategists, and Chief Data Officers.

Cohort Analysis Cohort analysis is a subset of behavioral analytics that takes the data from a given eCommerce platform, web application, or online game and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined timespan. Cohort analysis allows a company to ‘see patterns clearly across the lifecycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes.’ By seeing these patterns of time, a company can adapt and tailor its service to those specific cohorts. While cohort analysis is sometimes associated with a cohort study, they are different and should not be viewed as one in the same. Cohort analysis has come to describe specifically the analysis of cohorts in regards to big data and business analytics, while a cohort study is a more general umbrella term that describes a type of study in which data is broken down into similar groups. …

There are lots of ways to assess how predictive a model is while correcting for overfitting. In Caret the main methods I use are leave one out cross validation, for when we have relatively few samples, and k fold cross validation when we have more. There also is another method called ‘optimism corrected bootstrapping’, that attempts to save statistical power, by first getting the overfitted result, then trying to correct this result by bootstrapping the data to estimate the degree of optimism. A few simple tests in Caret can demonstrate this method is bunk.

Autoregressive Models for Matrix-Valued Time Series

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