Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs.
Computational linear algebra and optimization
Automatic differentiation via backward propagation
Momentum methods from ordinary differential equations
Conjugate gradient method
Ordinary and partial differential equations
Vector and tensor calculus
Convolutional neural networks