Section: Chosen (worldwide) problems solved with machine learning and the future of machine learning | Machine Learnig

Section outline

- Select activity In today's interconnected and fast-paced world, we...
  
  In today's interconnected and fast-paced world, we face a multitude of complex global challenges, ranging from climate change and healthcare disparities to poverty and food security. These problems often seem insurmountable, and their solutions require innovative approaches. Machine learning, a field of artificial intelligence, has emerged as a powerful tool in our arsenal for addressing these global issues. It offers the promise of not only understanding the intricacies of these challenges but also providing data-driven, scalable, and adaptable solutions.
  
  Machine learning leverages advanced algorithms and computational capabilities to process and analyze vast amounts of data, uncover hidden patterns, and make predictions. By doing so, it enables us to tackle global problems with a level of precision and efficiency that was once unimaginable. This transformative technology has the potential to revolutionize our approach to some of the most pressing issues that humanity faces.
  
  During lecture, we will delve into the ways in which machine learning is making an impact on a global scale, revolutionizing our strategies for problem-solving and contributing to a more sustainable and equitable world. We will explore its applications in areas like healthcare accessibility, poverty alleviation, and much more, shedding light on the remarkable possibilities that machine learning brings to the table as we work towards a brighter and more sustainable future.
- Select activity Module 4
  
  Module 4 SCORM package
- Select activity Test
  
  Test SCORM package
- Select activity ReferencesTitle: Machine Learning for Environmenta...
  
  References
  Title: Machine Learning for Environmental Monitoring.
  
  Authors: V. Gandomi, M. Haider.
  
  Publication: IEEE Geoscience and Remote Sensing Magazine, 2014.
  
  DOI: 10.1109/MGRS.2014.2359053
  
  Title: Predictive Analytics with Machine Learning in the Clinical Laboratory.
  
  Authors: S. Altschul, C. Rais-Bahrami, et al.
  
  Publication: Clinical Biochemistry, 2018.
  
  DOI: 10.1016/j.clinbiochem.2018.08.004
  
  Title: A Survey of Machine Learning in Big Data Processing.
  
  Authors: H. Chen, Y. Zhang, et al.
  
  Publication: IEEE Access, 2014.
  
  DOI: 10.1109/ACCESS.2014.2325029
  
  Title: Machine Learning in Agriculture: A Review.
  
  Authors: N. Chakraborty, A. Chatterjee, et al.
  
  Publication: Artificial Intelligence in Agriculture, 2019.
  
  DOI: 10.1016/j.aiia.2019.06.009
  
  Title: Machine Learning Approaches to Poverty Mapping.
  
  Authors: S. Lall, M. Dablander, et al.
  
  Publication: Proceedings of the National Academy of Sciences, 2018.
  
  DOI: 10.1073/pnas.1714545115
- Select activity Bibliography Xu, Y., Liu, X., Cao, X., Huang, C., ... (kopia)
  
  Bibliography
  Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.W. and Qiu, J., 2021. Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4).
  Zhou, Z.H., 2021. Machine learning. Springer Nature.
  Jordan, M.I. and Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and prospects. Science, 349(6245), pp.255-260.
  El Naqa, I. and Murphy, M.J., 2015. What is machine learning? (pp. 3-11). Springer International Publishing.
  Wang, H., Lei, Z., Zhang, X., Zhou, B. and Peng, J., 2016. Machine learning basics. Deep learning, pp.98-164.
  Hwang, G.J. and Chien, S.Y., 2022. Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, p.100082.