Explore the fundamentals of supervised learning algorithms, especially for
agricultural and natural science datasets. Implement and interpret Machine
Learning models, starting with statistical models. Learn how to choose and
divide your datasets (testing and training). Select appropriate variables.
Then brush up on your Python and move on to Deep Learning. Learn about the
XOR problem, back-propagation, and regularization. Learn what convolution
is, and use it to classify images. Learn about U-Net and ResNet, and their
applications in machine vision.
Extent:
7.5 hp
Prerequisites
Admitted to a postgraduate program in animal science, biology, veterinary
medicine, food science, nutrition, nursing, or related subjects, or to a
residency program in veterinary science. Alternatively, admitted to a
postgraduate program in Bioinformatics or Biological field with a
significant computational aspect. VMAS course Statistics I: Basic
Statistics or similar (including regression). Introduction to Python for
data science, PVG0045, or equivalent Python coursework or experience. If
you want clarification on what constitutes sufficient Python knowledge or
advice on how to obtain it, please contact the course coordinator.