Deep Learning Methods in Finance
Objectives
Present the area of deep learning.
Discuss different architectures and the guidelines for building a deep learning neural network.
Be able to implement and train a neural network in Keras.
General characterization
Code
400107
Credits
3.5
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
NA
Bibliography
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
Teaching method
Theoretical and practical classes.
Evaluation method
First epoch: deep learning project.
Second epoch: deep learning project.
The project is the same for the two epochs. The student can defend the project in one of the two epochs.
If a student fails the first epoch he/she is responsible for proposing a new project that must be validated by the professor.
The same applies to students that want to improve the grade.
Subject matter
Introduction to deep learning History and cognitive basis of neural computation.
The perceptron / multi-layer perceptron
The neural net as a universal approximator
Optimization by gradient descent
Back propagation Overfitting and regularization (Dropout)
Deep Learning for classification problems
Deep Learning for regression problems
Tuning of the hyperparameters
Keras callbacks