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This homework aims to evaluate the use of machine learning in research
This homework aims to evaluate the use of machine learning in research. You are required to find a publication (as recent as possible) published in one of the two top venues Science (https://www.sciencemag.org/) or Nature (https://www.nature.com/nature/articles). The articles' choice will be on a first come - first serve basis, and no student will cover the same article. As soon as you find the article you'd like to review, please enter the name of the article and the URL in this form: {link is not shared, Please provide the article title + link and I will add}
You are to submit a 10-15 slides presentation that presents the paper and evaluates machine learning as part of the research. Provide detailed technical information about the use of machine learning (data size, algorithms choice, platforms, optimizations, etc.) The evaluation should discuss how you'd repeat and improve the study
These articles are already chosen, please look for a different article:
| Development of machine learning model for diagnostic disease prediction based on laboratory tests |
| Early prediction of circulatory failure in the intensive care unit using machine learning |
| Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems |
| Machine learning prediction models for prognosis of critically ill patients after open?heart surgery |
| Super resolution DOA estimation based on deep neural network |
| Predicting breast cancer risk using interacting genetic and demographic factors and machine learning |
| A machine learning toolkit for genetic engineering attribution to facilitate biosecurity |
| Multimodal deep learning models for early detection of Alzheimer’s disease stage |
| Soft robot perception using embedded soft sensors and recurrent neural networks |
A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success |
| Assessing the potential for deep learning and computer vision to identify bumble bee species from images |
| Automated abnormality detection in lower extremity radiographs using deep learning |
| Machine learning-based prediction of COVID-19 diagnosis based on symptoms |
| Internal short circuit detection in Li-ion batteries using supervised machine learning |
| Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques |
| Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera |
| Predicting women with depressive symptoms postpartum with machine learning methods |
| Deep learning classification of lung cancer histology using CT images |
| Automatic classification of canine thoracic radiographs using deep learning |
| A deep learning approach to identify unhealthy advertisements in street view images |
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