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- University of Cambridge Bioinformatics Training22
- European Bioinformatics Institute (EBI)14
- Australian BioCommons5
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Keyword
- HDRUK22
- machine learning6
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- Machine Learning and Artificial Intelligence Course3
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- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
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Scientific topic
- Reinforcement learning
- Bioinformatics1085
- Genome annotation289
- Exomes286
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- Viral genomics286
- Whole genomes286
- Biological modelling229
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- Bottom-up proteomics145
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- Protein and peptide identification145
- Proteomics145
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- High-throughput sequencing35
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Venue
- Craik-Marshall Building22
- European Bioinformatics Institute, Hinxton4
- Computational and Data Driven Science1
- European Bioinformatics Institute1
- Fondazione Edmund Mach, Palazzo della Ricerca e della Conoscenza, Via E. Mach 1, San Michele all'Adige1
- Instituto Gulbenkian de Ciência1
- La Pedrera, 92, Passeig de Gràcia1
- Narva mnt 18, room 20341
- Provinciehuis Provincie Vlaams-Brabant Provincieplein 1 3010 Leuven Belgium1
- Provinciehuis, Provincieplein 1, Leuven1
- TBC1
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Target audience
- Institutions and other external Institutions or individuals22
- Postdocs and Staff members from the University of Cambridge22
- Graduate students20
- This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.16
- Researchers3
- This introductory course is aimed at biologists with little or no experience in machine learning.3
- <span style="color:#FF0000">After you have booked a place2
- Computational biologists2
- PhD students2
- Students and researchers from life-sciences or biomedical backgrounds2
- The course is open to Graduate students2
- if you are unable to attend any of the live sessions and would like to work in your own time2
- including for registered university students.<span style="color:#FF0000">2
- or will shortly have2
- please email the Team as Attendance will be taken on all courses. A charge is applied for non-attendance2
- post-docs2
- the need to apply the techniques presented during the course to biomedical data.2
- who have2
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £350. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £175. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £400. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £200. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- Beginner1
- Bioinformaticians1
- Computer science1
- Experimental Researchers1
- Life Science Researchers1
- Master students1
- PhD Students1
- Postdoctoral students1
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- This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses. Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Python turorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImage Archive. The course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python. Prerequisite reading: BioImage Archive: A call for public archives for biological image data ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy The BioStudies database - one stop shop for all data supporting a life sciences study EMPIAR: a public archive for raw electron microscopy image data Image Data Resource: a bioimage data integration and publication platform BioImage Model Zoo 1
- This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required1
- This workshop is aimed at researchers and bioinformaticians from across industry and academia who are looking to leverage machine learning approaches in protein function prediction. It will guide participants through the use of big data to build analytical workflows on publically-available biological data. Participants will require prior experience in the use of the command line interface and confidence in a programming language to fully benefit from the workshop. Please contact us if you have any questions about the course's suitability before you apply.1
- bioinformaticians1
- biological data analysts1
- students1
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- English1
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