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Deep Learning and Neural Networks for Financial Engineering

Expand your machine learning toolkit to include deep learning techniques, and learn about their applications within finance.

Deep Learning and Neural Networks for Financial Engineering
This course is archived
Estimated 7 weeks
4–6 hours per week
Progress at your own speed
Cost to Enroll
$799 USD

About this course

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Deep Learning ventures into territory associated with Artificial Intelligence. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Students will gain an understanding of deep learning techniques, including how alternate data sources such as images and text can advance practice within finance.

At a glance

  • Institution: NYUx
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    The course is intended for financial professionals (analysts, portfolio managers, traders, quants, advisers) and other practitioners with an interest in finance. Solid programming skills are advised; knowledge of Python is an advantage. Students should also have knowledge of basic probability, statistical techniques (including linear regression), calculus; linear algebra. A background (perhaps through the first course of this series) in Classical Machine Learning is helpful but not mandatory.

What you'll learn

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By the end of this course students should be able to:

  • utilize neural network and deep learning techniques and apply them in many domains, including Finance

  • make predictions based on financial data

  • use alternate data sources such as images and text and associated techniques such as image recognition and natural language processing for prediction

  • use these techniques and data for

    • optimizing portfolios and portfolio management

    • managing risk

    • streamlining operations

Week 0: Classical Machine Learning: Overview

  • Guided entry for students who have not taken the first course in the series

  • Notational conventions

  • Basic ideas: linear regression, classification

  • Recipe for Machine Learning

Week 1: Introduction to Neural Networks and Deep Learning

  • Neural Networks Overview

  • Coding Neural Networks: Tensorflow, Keras

  • Practical Colab

Week 2 : Convolutional Neural Networks

  • A neural network is a Universal Function Approximator

  • Convolutional Neural Networks (CNN): Introduction

  • CNN: Multiple input/output features

  • CNN: Space and time

Week 3: Recurrent Neural Networks

  • Recurrent Neural Networks (RNN): Introduction

  • RNN Overview

  • Generating text with an RNN

Week 4: Training Neural Networks

  • Back propagation

  • Vanishing and exploding gradients

  • Initializing and maintaining weights

  • Improving trainability

  • How big should my Neural Network be ?

Week 5: Interpretation and Transfer Learning

  • Interpretation: Preview

  • Transfer Learning

  • Tensors, Matrix Gradients

Week 6: Advanced Recurrent Architectures

  • Gradients of an RNN

  • RNN Gradients that vanish and explode

  • Residual connections

  • Neural Programming

  • LSTM

  • Attention: introduction

Week 7: Advanced topics

  • Neural Language Processing (NLP)

  • Interpretation: what is going on inside a Neural Network

  • Attention

  • Adversarial examples

  • Final words

About the instructors

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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