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