## Major types of machine learning

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 …

## Introduction to machine learning

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 …

## From regression to classification

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 the course

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 …

## How to assess the goodness of fit in classification?

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., …

## Main types of regularization

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

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 …

## Derivation of linear regression

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. …

## What is Linear Regression?

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 …

## Summary of Week 2

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 …

## Supervised learning framework

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 …

## Machine learning transforms the landscape of quantitative finance

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

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 …

## Implementing gradient descent in Python

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 …