wahidulalamriyad/oxford-machine-learning-summer-school
ML x Health consists of Bayesian ML, Representation Learning, Graph Neural Networks, Computer Vision, Knowledge Graphs, Knowledge-Aware ML, Symbolic Reasoning, Neuro-Symbolic AI, EHR, Imaging, Genomics, and Multi-Omics. ML x Finance consists of Gaussian Processes, Time Series, Multi-Lingual NLP, Reinforcement Learning, Building Market Simulators, Trading, Asset Management, Financial Inclusion, ESG, Emerging Risks, and Economic Prosperity. Achievement - Completing all tasks before the deadline with 100% output and achieving the Certificate of Completion. Health and Finance Tracks were crucial in completing the MSc Data Science Final Year Dissertation.
This project is a personal record of completing the Oxford Machine Learning Summer School, showcasing expertise in applying machine learning to real-world challenges in healthcare and finance. It outlines the specific machine learning techniques used for tasks like analyzing electronic health records, genomic data, or financial time series. This record would be useful for academics, researchers, or professionals looking to demonstrate their understanding of advanced machine learning applications in these fields.
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Use this if you are an academic or professional who has completed the Oxford Machine Learning Summer School and want to showcase your achievements and the specific ML techniques mastered in health and finance.
Not ideal if you are looking for a standalone machine learning tool or a comprehensive guide on how to implement ML models from scratch.
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Sep 10, 2022
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