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Scientific topics: Machine learning 

Machine Learning & BioStatistics Hackathon 2020

Materials created at the Machine Learning and BioStatistics hackathon organised by ELIXIR-GR (CERTH) in October and November 2020.

Scientific topics: Computer science, Statistics and probability, Machine learning

Keywords: machine learning, biostatistics, eLearning, EeLP

Resource type: Training materials

Machine Learning & BioStatistics Hackathon 2020 http://tess.elixir-uk.org/materials/machine-learning-biostatistics-hackathon-2020 Materials created at the Machine Learning and BioStatistics hackathon organised by ELIXIR-GR (CERTH) in October and November 2020. Computer science Statistics and probability Machine learning machine learning, biostatistics, eLearning, EeLP Life Science Researchers statisticians Training Designers Training instructors Trainers
WEBINAR: Getting started with deep learning

This Zenodo record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021.

Scientific topics: Machine learning

Keywords: Deep learning

WEBINAR: Getting started with deep learning http://tess.elixir-uk.org/materials/webinar-getting-started-with-deep-learning This Zenodo record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021. Machine learning Deep learning
A tour of machine learning - classification

Machine learning has become ubiquitous in biotechnology (as in many other fields), fueled largely by the increasing availability and amount of data. Learning algorithms can figure out how to perform important tasks by generalizing examples. Typical applications are diagnoses/prognoses,...

Scientific topics: Machine learning

A tour of machine learning - classification http://tess.elixir-uk.org/materials/a-tour-of-machine-learning-classification Machine learning has become ubiquitous in biotechnology (as in many other fields), fueled largely by the increasing availability and amount of data. Learning algorithms can figure out how to perform important tasks by generalizing examples. Typical applications are diagnoses/prognoses, gene/protein annotation, drug design, image recognition, text mining and many others. However, building successful machine learning models requires a substantial amount of “black art” that is hard to find in textbooks. This course is an interactive Jupyter Notebook (Python) that will teach you how to build successful machine learning models. No background in machine learning is assumed, just a keen interest. Machine learning Life Science Researchers PhD students beginner bioinformaticians post-docs 2016-04-22
Deep Learning using a Convolutional Neural Network

This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not...

Scientific topics: Machine learning

Resource type: Video

Deep Learning using a Convolutional Neural Network http://tess.elixir-uk.org/materials/deep-learning-using-a-convolutional-neural-network This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not accessible through modelling and simulation often performed in HPC. Traditional learning, which was introduced in the 1950s and became a data-driven paradigm in the 90s, is usually based on an iterative process of feature engineering, learning, and modelling. Although successful on many tasks, the resulting models are often hard to transfer to other datasets and research areas. Machine learning PhD students Post Docs
Introduction to Machine Learning Algorithms

This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data.

Scientific topics: Machine learning

Resource type: Video

Introduction to Machine Learning Algorithms http://tess.elixir-uk.org/materials/introduction-to-machine-learning-algorithms-b1434ce7-b934-4b48-af7c-0274e2c37815 This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data. Machine learning PhD students Post Docs
Introduction to Machine Learning Algorithms

This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data.

Scientific topics: Machine learning

Resource type: PDF

Introduction to Machine Learning Algorithms http://tess.elixir-uk.org/materials/introduction-to-machine-learning-algorithms This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data. Machine learning PhD students Post Docs