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!
