Course dates: 18th-22nd of March 2019
Location: Centre for Virus Research, Garscube Campus, University of Glasgow, Glasgow, UK
The field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of DNA sequence data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine Learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyze complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well.
Nichole Bennett will be giving a 5-day course, which cover basics of the Python programming language as well as the pandas and sklearn Python libraries for data wrangling and machine learning. By the end of this course, participants will understand: How to input and clean data in Python using the pandas library How to perform exploratory data analysis in Python How to use the sklearn library in Python for machine learning workflows How to choose an appropriate machine learning model for the task How to use supervised machine learning models (SVM, Decision Trees, Neural Networks, etc.) for classification tasks How to use unsupervised machine learning models for clustering tasks How to evaluate machine learning models and interpret their results This course is intended to give participants a conceptual overview of machine learning algorithms and an intuition for the mathematics underlying them, equipping participants to be able to choose and implement appropriate models for biological datasets.
Simon Babayan – Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow and The Moredun Research Institute: “Let the biology teach the machine: using machine learning to identify determinants of disease in omics data”
Students will work on their own laptops and will be required to do some program installation prior to the course. The enrolment will be limited to 16 participants and preference will be given to applicants who already have some programming expertise.
Nichole Bennett researches science communication in the Ph.D. program at The Stan Richards School of Advertising and Public Relations at The University of Texas. Through STEMprov, she teaches scientists improv to boost their communication skills (http://stemprov.org). She is also a coding teacher for Long-View Micro School and has her Masters in Ecology, Evolution, and Behavior from The University of Texas. She has taught multiple courses on Data Science/Machine Learning/Python/Statistics for bootcamps and industry. When not doing those things, you can find Nichole performing and teaching improvisational acting. You can follow her on Twitter @choleness or Instagram at @theawkwardoff.
£250 for the 5-day course including lunches. To apply fill in the questionnaire before Monday 18th of February. You will be contacted within a week of submission with a response. For further information, contact the local organiser Joseph Hughes (joseph(dot)hughes(at)glasgow(dot)ac(dot)uk).
NB: Participants are responsible for their own travel arrangements and accommodation.
photo credit Steve Rogers Photography