The coronavirus has become a major epidemic for the world causing more than 24,073 deaths worldwide. Researchers & developers across the globe are trying there best to contained & prevent COVID-19, the world’s one of renowned University the Stanford University has also putting vital contributions against COVID-19.
Stanford University has now offering CS472 data science and AI for COVID-19. This course is project-based & it focuses on investigates and models COVID-19 using tools from data science and machine learning.
This course will introduce the relevant background for the biology and epidemiology of the COVID-19 virus. Then you will critically examine current models that are used to predict infection rates in the population as well as models used to support various public health interventions (e.g. herd immunity and social distancing).
The core of this class will be projects aimed to create tools that can assist in the ongoing global health efforts. Potential projects include data visualization and education platforms, improved modeling and predictions, social network and NLP analysis of the propagation of COVID-19 information, and tools for good health.
The class is aimed toward students with experience in data science and AI and will include guest lectures by biomedical experts. Prerequisites for this course will be background in machine learning and statistics for that you can refer to CS229, STATS216 or equivalent and some biological background is helpful but not required.
The example projects include improving epidemiological forecasting models, model and predict the impact of various intervention policies, analyze how COVID-19 information spreads on the Twitter, platform to securely share COVID-19 patient features and outcomes and COVID-19 genomic analysis.
The Instructor for this course will be Prof. James Zou (jamesz at Stanford), TA: Zhenqin (Michael) Wu (zqwu at Stanford), TA: Jaime Roquero Gimenez (roquero at Stanford).
Date Lecture Readings/notes
April 10 – Overview of COVID-19 and health systems during pandemic Nigam Shah guest
April 17 – Epidemiological predictions and modeling
April 24 – Infectious disease background of COVID-19 Michele Barry guest
May 1 – Project proposal
May 8 – ML for COVID-19 drugsRuss Altman guest
May 15 – COVID-19 genomic analysis Julia Palacios guest
May 22 – Project milestone presentation
May 29 – NLP analysis COVID-19 information on Twitter and FB
June 5 – Final demos
Link to the course –
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