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