Machine Learning in Finance

Objectivos

- Understand the design principles of neural networks;

- Understand the concept of activation function;

- Understand the backpropagation algorithm for training a neural network;

- Being able to build a neural network to solve classification tasks;

- Being able to use Keras or similar libraries to build a Neural Network;

- Understand the convolution operator and the idea behind convolutional neural network;

- Understand the main principles of recurrent neural network; 

- Understand LSTM and how they can be applied to counteract vanishing gradient problem.

- Being able to apply one of the deep model presented to solve financial classification or regression tasks.

Caracterização geral

Código

400103

Créditos

7.5

Professor responsável

Mauro Castelli

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

N/A

Bibliografia

Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.

Método de ensino

Theoretical and practical classes.

Método de avaliação

First epoch: project with discussion.

Second epoch: project with discussion.

Conteúdo

Single perceptron and the training process;

Neural Networks with hidden layers and the backpropagation algorithm;

Convolutional Neural Networks;

Applicatio of CNN to image analysis;

Recurrent Neural Networks;

Vanishing gradient and LSTM

Application of LSTM to time series analysis

 

Cursos

Cursos onde a unidade curricular é leccionada: