[Colab notebook - part 1](https://colab.research.google.com/drive/1_uemRwNuok1wop6wP2Aiokn0KQgcwfr1?usp=sharing)
[Colab notebook - part 2](https://colab.research.google.com/drive/1PiUee4n8T7pIhDV5zDEqhsK5jXvDYHpO?usp=sharing)
[Colab notebook - part 3](https://colab.research.google.com/drive/1kIbrUtNH_WIPQX5vLyzRjs5CTgKA2CMT?usp=sharing)
[Colab notebook - part 4](https://colab.research.google.com/drive/10wEN9eqdE9Z7CtvhRFgsL3gAzunZGlee?usp=sharing)
# Introduction
# Introduction
Standard machine learning approaches require to have a centralizaed dataset in order to train a model. In certain scenarios like in the biomedical field, this is not straightforward due to several reasons like:
Standard machine learning approaches require to have a centralizaed dataset in order to train a model. In certain scenarios like in the biomedical field, this is not straightforward due to several reasons like:
...
@@ -58,6 +46,20 @@ The main challenges in FL are associated to:
...
@@ -58,6 +46,20 @@ The main challenges in FL are associated to:
-**Security:** adversarial attacks and data leakage.
-**Security:** adversarial attacks and data leakage.
This lecture aims at covering the statistical background required to perform association analysis in typical studies of heterogeneous information. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group.
This lecture aims at covering the statistical background required to perform association analysis in typical studies of heterogeneous information. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group.
