Deep Learning Methods in Finance
Objetivos
The course will introduce the general concept of Machine Learning and the whole ML process will be fully analyzed.
At the end of the course the students should be able to:
- Apply preprocessing techniques to raw data
- Understand the concepts of cross-validation, train/test split, leave-one-out
- Understand model evaluation metrics
- Apply regression and classification techniques to build predictive models
- Understand the parameters tuning process
- Apply the Machine Learning process to analyze real financial data
Caracterização geral
Código
400107
Créditos
3.5
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
No requirements, but basic understanding of programming and statistics are expected.
Bibliografia
Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Othe research papers provided by the professor.
Método de ensino
Theoretical and practical classes.
Método de avaliação
First epoch: Project related to the application of ML techniques to analyze financial data.
Second epoch: Project related to the application of ML techniques to analyze financial data.
Conteúdo
Machine Learning: definitions and introductory concepts
The machine learning task: preprocesing, model construction and validation
Evaluation metrics
Cross validation and train/test validation. Model complexity and overfitting
Classification task: logistic regression
KNN algorithm and K-means algorithm
Linear Regression
Support Vector Machines
Random Forests, Decision Tress and Ensemmle techniques