How can we eradicate malaria? Explore cutting-edge science and technology, and examine policies needed to control and eliminate malaria.
What you'll learn
Gain insights on the biology of the malaria parasite, the mosquito vector, and the human host
Understand how new genomic tools and molecular epidemiology approaches can improve decision-making to inform local and national malaria policymaking
Explore the impact of drug and insecticide resistance on malaria control and eradication programs and advances in vaccine and drug development
Apply modern data science strategies to measure how well different malaria interventions work in different settings
Maximize the impact of available resources through enhanced targeting and improved efficiency of malaria interventions
Incorporate core principles of adaptive surveillance systems to monitor and respond to malaria for the purpose of disease elimination and attainment of malaria…
Understand the historical, political, behavioral, and socio-economic contexts of malaria control, elimination, and eradication efforts
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…