A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this! Calculus is not even required.

A third to half the jobs that we are currently employed in would disappear in the next 15 years; and yet your child is being prepared in school for those very same jobs that won't exist by the time they graduate. Our curriculum prepares us for a lifetime career, but a child today can expect to change jobs at least seven times over the course of their lives – and five of those jobs don't exist yet. The coming days would see us pursuing careers that we cannot even imagine today. For instance your child could be an expert licensed drone pilot, or a cyber warrior in the army, a data analyst making sense of the peta bytes of data generated through our social interactions and trying to forecast our behavior. The other big challenge facing students today is that the velocity of technology changes has gained incredible speed; this is making knowledge obsolete faster than before.

The current study examined the degree to which the quality and characteristics of students’ essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students’ essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students’ holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

Assessment in intelligent tutoring system (ITS) on concept maps (CM) matches an expert CM to a learner CM. Feedbacks are provided to the learner as semantic comments andvisual corrections. In this paper, quality of feedbacks is improved by using an ontological semantic for matching, formalized as a correlation feedback. Matchings are selected based on an overall assignment solution providing a suboptimal set of correlation feedbacks to the learner.

With the inexorable expansion of the semantic layer on the Web and its ecosystem of connected applications, the global citizens expect more and more data expositions coming from public activities. The recent developments in knowledge representation and reasoning push public structures to deploy their data warehouses in parallel of classical websites exhibitions. This article presents an infrastructure to spread the descriptions of scholarships. After introducing the major contributions concerning the semantical annotation of materials occurring in recruitment processes, we describe our case study about the strategy of the University of Sassari concerning the expositions of academical grants. Supported by a core and aligned ontology of the domain we present our prototypical architecture to support and gather the spread of scholarships.

Intelligent tutoring systems for ill-defined domains, such as reading and writing, are critically needed, yet uncommon. Two such systems, the Interactive Strategy Training for Active Reading and Thinking (iSTART) and Writing Pal (W-Pal) use natural language processing (NLP) to assess learners’ written (i.e., typed) responses and provide immediate, accurate feedback. The current paper reports on efforts to implement adaptive instruction and task selection into both systems. In iSTART, we developed a new practice module, in which learners’ past performance data governs two adaptive functionalities: 1) the use of self-explanation scaffolding and 2) the increase or decrease of difficulty of practice texts. In W-Pal, adaptivity is implemented by triggering targeted instructional support on the basis of deficits identified in learners’ essays. In this paper, we describe the need for adaptive reading and writing instruction, along with the design and development of adaptivity in the two systems.

At the core of an intelligent tutoring system is the ability to estimate a student’s level of skill proficiency. However, making accurate skill estimates can require asking the student relatively many questions. We address this challenge by using “transfer learning,” a field of machine learning which uses data from related, but different, “source” domains to aid in learning in a poorly labeled “target” domain. Thus, to predict the skill of a student who hasn't answered many “target” skill questions, we use estimates of well tested “source” skills. We explore settings where the student has answered no questions related to the target skill (the cold start setting) and those where she has answered a few (the warm start setting). We focus on the challenging situation where the domain expert has not identified the relationship between the skills. We find that the Ridge estimator is useful for transferring knowledge from source to target skills, outperforming nonparametric regression methods and a baseline which only uses student performance on target skill questions.

Interaction between humans and machines, like social robots, requires real time recognition of human actions. Most approaches to this problem wait for the end of the gesture to perform classification. In this paper we present a deep learning approach to online gesture recognition that allows for an estimation of the current gesture since its beginning. Our approach is to modify the existing Temporal Deep Belief Network (TDBN) architecture. The result is a Discriminative Temporal Deep Belief Network (DTDBN) which we apply to the online classification of motion capture streams. We optimize and evaluate our model in comparison with related work.

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.