Machine Learning in Finance
There are no formal prerequisites for the course, and we’ll endeavour to explain the foundational concepts during the training if required. However, since data science and machine learning rely on the following disciplines, it is good to brush up on:
- Linear algebra — we are dealing with datasets consisting of many data points and algorithms with many (hyper)parameters; linear algebra is the essential language in this multivariate setting;
- Probability theory — many of the models are versed in the language of probability: for example, disturbances in linear regression models are random variables; frequentist likelihoods and Bayesian priors and posteriors are probabilities; Somewhat less pertinently:
- Information theory — this branch of applied mathematics is concerned with quantifying how much information is present in our inputs; in machine learning we are concerned with extracting as much information as possible;
- Numerical computation — many of the algorithms rely on numerical methods rather than analytical solutions; in practice care is needed to avoid numerical issues such as overflow and underflow, poor conditioning, etc.;
- Optimisation theory — much of machine learning is concerned with optimising (hyper)parameters and therefore utilises the machinery from optimisation theory, such as gradient-based optimisation.
In order to practise machine learning, one needs
- A working knowledge of a convenient programming language, such as Python or R;
- Familiarity with relevant libraries.
- During our trainings, all code demonstrations will be using Python.
This event is in conjunction with The Thalesians.
The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.