Deep Learning Methods in Finance


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





Responsible teacher

Mauro Castelli


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




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


Programs where the course is taught: