Graphical Models or Probabilistic and Unsupervised Learning. What course should I take?

I am interested in pursuing a career as a quantitative researcher at a hedge fund/asset manager. I have to take either 'Graphical Models' or 'Probabilistic and Unsupervised Learning' as part of my masters degree in machine learning, the syllabus for the two courses are as follows:

Graphical Models: -Bayesian Reasoning; -Bayesian Networks; -Directed and Undirected Graphical Models; -Inference in Singly-Connected Graphs; -Hidden Markov Models; -Junction Tree Algorithm; -Decision Making under uncertainty; -Markov Decision Processes; -Learning with Missing Data; -Approximate Inference using Sampling; -If time permits we will also cover some deterministic approximate inference

Probabilistic and Unsupervised Learning: -Basics of Bayesian learning and regression; -Latent variable models, including mixture models and factor models; -The Expectation-Maximisation (EM) algorithm; -Time series, including hidden Markov models and state-space models; -Spectral learning; -Graphical representations of probabilistic models; -Belief propagation, junction trees and message passing; -Model selection, hyperparameter optimisation and Gaussian-process regression

Thanks for any advice!