Machine Learning in Finance
Objectives
- 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.
General characterization
Code
400103
Credits
7.5
Responsible teacher
Mauro Castelli
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
N/A
Bibliography
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
Teaching method
Theoretical and practical classes.
Evaluation method
First epoch: project with discussion.
Second epoch: project with discussion.
Subject matter
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