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This course covers the fundamentals of machine learning, its applications to geoscientific problems, and it provides basic best practices for the rigorous development and evaluation of machine learning models. The main focus of the course is on describing the fundamental theory of linear regression, logistic regression, neural networks, convolutional neural networks, sequence modelling, dimensionality reduction, generative modelling, and physics-inspired neural networks. Students will also be introduced to practical applications in geoscience for each of the presented methods; lab sessions will be held using the PyTorch computational framework in the Python programming language.
Lectures
Sunday, and Wednesday, 1:00pm - 2:30pm
Teaching Staff
- Instructor: Matteo Ravasi - Office Hours: Monday 4pm to 5pm (by Appointment: B1-3142)
Textbook
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville – MIT Press.
Pre-requisites
Knowledge of calculus, linear algebra ad statistics is required. Basic Python knowledge is preferred.