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! P.S. Please see the updated list of optional modules I have attached in picture format. I have to take 45-75 from the top group and then 45-15 credits from the bottom. Overall I must choose 90 credits #HedgeFund #Quant #AI #MachineLearning
What school? I'd take graphical models and expand your understanding on your understanding on Bayesian principles and Decision Models/making . Also if you can take both do it NLP and deep learning are fringey
This is for University College London in the U.K. it is their Computational Statistics and Machine Learning programme. There are options to take up to three finance related modules including Stochastic Methods in Finance I & II and a Financial Engineering course. From what I have read, Stochastic Processes used mainly in derivative pricing is no longer the main role of Quantitative Researchers (or atleast the skillset is not as sought after as say pre-08) Why do you say DL and NLP are fringey?
Sounds unsupervised one is more a fundamental course and might be helpful in interview/ jobs. The graphical models sounds more advanced and usually people in industry do not know this type of topic !!!
Would you reccommend taking both, inplace of something like Deep Learning or NLP? Someone below suggested this as an option
Graphical models sounds more like classical statistics, I believe that is more relevant to quant roles.
Would you reccommend it over say NLP or Deep Learning? I have uploaded a screenshot of the list of available modules
Hi Op sorry forgot to reply. I would prefer pgm over deep learning if you’re looking for relevance to quant finance.
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Honestly take both if you can
Even at the expense of courses such as statistical NLP/ Deep Reinforcement Learning?
What courses are you taking in total? That will make it easier for us to answer your question. I suggested both because they’re quite different but both subjects are used in quant finance. I haven’t heard Nlp being used directly in qf but it might be used in alternative data funds.