The course work will be in English and will teach the students to solve problems in large database using machine-learning systems. Machine-learning paradigms for selecting input variables. Data Mining: Practical Machine Learning Tools and applications. Applications in agricultural production.
Newly published paper will be used to guide the problem solving techniques. Please see some examples below:
BORCHERS, M.R. ; CHANG, Y.M.; PROUDFOOT, K.L.; WADSWORTH, B.A.; STONE, A.E.; BEWLEY, J.M. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science, v. 100, n. 7, p. 5664-5674, 2017. ISSN 0022-0302, https://doi.org/10.3168/jds.2016-11526.
McQUEEN, Robert J. et. Al. Applying Machine Learning to Agricultural Data. 1994.https://researchcommons.waikato.ac.nz/bitstream/handle/10289/1142/uow-cs-wp-1994-13.pdf?sequence=1
MUTTIL, N.; CHAU, Kwok-Wing. Machine-learning paradigms for selecting ecologically significant input variables. Engineering Applications of Artificial Intelligence, v. 20, n. 6, p. 735-744, 2007. ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2006.11.016.
VALLETTA, J.J.; TORNEY, C.; KINGS, M.; THORNTON, A.; MADDEN, J. Applications of machine learning in animal behaviour studies. Animal Behaviour, v. 124, p. 203-220, 2017. ISSN 0003-3472, https://doi.org/10.1016/j.anbehav.2016.12.005.