Course: An Introduction to Machine Learning in Quantitative Finance

In this article, we introduce the major categories of machine learning. Supervised Learning: it aims to learn from a training set containing labelled data (that is, containing both inputs and …

Big Data: Technological and social changes have transformed the amount and types of data that are produced, and our capacity to process them. We have: Volume of data Variety of …

In this video, Prof. Hao Ni explains how to extend the regression framework to solve the classification problem. The framework of the classification is similar to the regression framework, which …

Welcome to this course, “An Introduction to Machine Learning in Quantitative Finance”, from the course leaders, Professor Hao Ni, Dr Camilo Garcia Trillos and Dr Alex Tse of University College …

For classification tasks, the accuracy and confusion matrix are the most popular metrics to quantify the goodness of fit. For the binary classification, one may consider other evaluation methods, e.g., …

The universe of the regularized linear regression methods is divided into different categories according to the norm of parameters in the penalty term. Recall that to resolve the overfitting issue, …

Overfitting Issue Overfitting refers to the case where the model prediction corresponds too closely or exactly to a particular data set (typically the training data), but may fail to fit …

One great advantage of OLS is that it yields an analytic formula for optimal model parameters. Let (hat{theta}) denote the estimator for linear coefficients (theta) of the linear regression model. …

Ordinary Least Square (OLS) is the simplest linear regression method with wide applications. OLS assumes the linear relationship between the input and the output and takes the mean squared error …

This week, we have introduced a general framework of supervised learning and focused on linear regression. We start with linear regression as an example to go through the key components …

In the previous steps, we introduced a framework for both regression and classification. In this video, let me summarize the general supervised learning framework and provide a comparison between the …

History of Quantitative Finance We can describe quantitative finance as the use of mathematical and statistical methods in the financial context, with applications in areas like pricing, trading, risk management …

Overview on Option Pricing What is an option? In financial markets, there are a huge number of various traded assets. One can divide them into two categories: primary assets and …

Automatic differentiation is one of the core functions of deep Learning libraries such as Tensorflow and Pytorch. To facilitate the training of the neural networks, it is required to memorize …

Gradient Descent Gradient Descent (GD) is a first-order iterative optimization algorithm for finding the local minimum of a function, which can be applied to tackle numerical optimization. We start with …