697 research outputs found
AI Education: Machine Learning Resources
In this column, we focus on resources for learning and teaching three broad categories of machine learning (ML): supervised, unsupervised, and reinforcement learning. In ournext column, we will focus specifically on deep neural network learning resources, so if you have any resource recommendations, please email them to the address above. [excerpt
On Approximate Nonlinear Gaussian Message Passing On Factor Graphs
Factor graphs have recently gained increasing attention as a unified
framework for representing and constructing algorithms for signal processing,
estimation, and control. One capability that does not seem to be well explored
within the factor graph tool kit is the ability to handle deterministic
nonlinear transformations, such as those occurring in nonlinear filtering and
smoothing problems, using tabulated message passing rules. In this
contribution, we provide general forward (filtering) and backward (smoothing)
approximate Gaussian message passing rules for deterministic nonlinear
transformation nodes in arbitrary factor graphs fulfilling a Markov property,
based on numerical quadrature procedures for the forward pass and a
Rauch-Tung-Striebel-type approximation of the backward pass. These message
passing rules can be employed for deriving many algorithms for solving
nonlinear problems using factor graphs, as is illustrated by the proposition of
a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented
message passing rules
Nonlinear regression in tax evasion with uncertainty: a variational approach
One of the major problems in today's economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate
Sequence Modelling For Analysing Student Interaction with Educational Systems
The analysis of log data generated by online educational systems is an
important task for improving the systems, and furthering our knowledge of how
students learn. This paper uses previously unseen log data from Edulab, the
largest provider of digital learning for mathematics in Denmark, to analyse the
sessions of its users, where 1.08 million student sessions are extracted from a
subset of their data. We propose to model students as a distribution of
different underlying student behaviours, where the sequence of actions from
each session belongs to an underlying student behaviour. We model student
behaviour as Markov chains, such that a student is modelled as a distribution
of Markov chains, which are estimated using a modified k-means clustering
algorithm. The resulting Markov chains are readily interpretable, and in a
qualitative analysis around 125,000 student sessions are identified as
exhibiting unproductive student behaviour. Based on our results this student
representation is promising, especially for educational systems offering many
different learning usages, and offers an alternative to common approaches like
modelling student behaviour as a single Markov chain often done in the
literature.Comment: The 10th International Conference on Educational Data Mining 201
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