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The DISI Centre and the Department of Physics & Astronomy (P&A) ran a 5-day intensive course for the department's PhD students titled 'Introduction to Machine Learning'.

Dr Nikos Nikolaou led the course, and the Teaching Assistant team consisted entirely of current and former students (now postdocs) of our Centre. All course material was created from scratch and included extensive hands-on tutorials introducing the students to the latest tools for practical implementation of machine learning algorithms.  

The theoretical part of the course covered a wide range of topics, from basic principles to advanced topics such as interpretable machine learning and deep learning architectures for imaging, time-series and natural language processing. Approximately 20 students attended the course (despite being optional, taking place mid-summer and during a heatwave). Based on the anonymous pre-course survey [1], most of the enrolled students had little to no contact with Machine Learning (ML). The average self-rating of the responders' overall experience with ML was 1.58/5. When asked whether they were familiar with 10 theoretical ML terms, responders had heard of only 1.58/10 and could give a definition for 0.58/10, on average. Based on the responses to a post-course survey, 100% of the responders characterised the course as 'helpful' and 'would recommend it to others', and they expressed a strong interest, after taking this course to explore ML methods in their own research.