Casting a Wider (Neural) Net: Introducing Data Science and Machine Learning to a Larger Audience
A presentation introducing foundational concepts related to Data Science and Machine learning was given five times over a one year period to audiences with different backgrounds. This report summarizes the successes and failures in delivering this material. Insights about the topic and useful teaching resources are presented.
Machine Learning for Business Students: An Experiential Learning Approach
This paper reports on the design and teaching of a course on Machine Learning (ML) for business students, focusing on the interaction between content and teaching approach. We demonstrate that the nature of ML technology is amenable to experiential learning and is well suited for business students as users of the technology. The community-based tools, documentation and datasets enable non-programmers to use and adapt open, public-domain ML examples. Students learn how to select algorithms for specific data and tasks, experiment with hyper-parameters and neural-network structures, evaluate results and interpret their business implications. We conclude that business students, at least in our school, often have good abstract understanding of computing and may be ready for deeper learning of digital technology. The general-purpose nature of ML makes it suitable for real-world business problems, making it business-relevant technology that should be introduced into business schools.
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.