Schedule - ML in Geoscience

Lecture	Topic	Exercise
1	Course overview and introduction to Machine Learning	-
2	Linear algebra refresher	-
3	Probability refresher	-
4	Gradient-based optimization	link
5	Linear and Logistic regression	link
6	Neural Networks: perceptron, activation functions	link1
7	Neural Networks: backpropagation, initialization, and loss functions	-
8	Best practices in training of Machine Learning models	-
9	Advanced solvers: momentum, RMSProp, Adam, greedy training	-
10	UQ in Neural Networks and Mixture Density Networks	link1 link2
11	Introduction to CNNs	-
12	CNNs Popular Architectutues	link
13	Sequence modelling: basic principles
14	Sequence modelling: architectures	link
15	Dimensionality reduction
16	Generative modelling and VAEs reduction
17	GANs
18	Scientific ML and PINNs	link
19	Deep learning for Inverse Problems
20	Invertible Neural Networks
21	Implicit Neural Networks