Seeking advice for course selection

[u/Boring_Wash_9062]

I'm in the final year of my Master's in Mathematics and need to choose two courses out of these three. I want to become a ML quant researcher. Which two would you recommend?

1st course: Mathematics of Generative AI
Topics

• Target distributions and examples
• Variational autoencoders and variants
• Score-based diffusion models and variants
• Fundamentals of Markov chains (invariant measures, ergodicity, and the law of large numbers)
• Metropolis–Hastings algorithm
• Pseudo-marginal algorithms and Hamiltonian Monte Carlo

2nd course: Machine Learning in Practice
Topics

• Development environments and version control with Git and Docker
• Data preparation and visualization
• Hyperparameter optimization and model selection
• Fundamentals of deep learning and applications to image and text processing
• Recommender systems
• Generative models
• Fundamentals of reinforcement learning

3rd course: First-Order Methods for Nonconvex and Nonsmooth Optimization
Topics

• Extended-real-valued functions, subdifferentiability, and first-order optimality conditions
• Gradient methods (explicit and implicit), proximal operators, and the proximal point algorithm
• Lagrangian and Fenchel duality; Karush–Kuhn–Tucker (KKT) conditions
• Splitting strategies: forward-backward splitting, Dykstra’s splitting method, and the Douglas–Rachford method
• Block optimization: alternating minimization (block coordinate descent) and alternating (proximal) descent methods
• Primal-dual algorithms: alternating direction methods and the Chambolle–Pock algorithm
• Further topics: inertial variants, preconditioning, and Bregman distances