Machine Learning in Finance
Código
400103
Unidade Orgânica
NOVA Information Management School
Créditos
7.5
Professor responsável
Mauro Castelli
Língua de ensino
Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês
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.
Pré-requisitos
N/A
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
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.