Machine Learning

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Statistical Machine Learning deals with data analysis. Basically it considers three kind of problems:

Unsupervised classification or clustering, where the objective is to find the natural groups that can be found in a dataset. It is an exploratory task.
Supervised classification tries to build predictive models from a dataset of classified objects. Basically given a dataset, the objective is to construct a probabilistic model able to predict the value of unseen cases.
Feature subset selection. In this case the objective is to select the most relevant characteristics to construct a classification model.

Probabilistic graphical models are the most commonly used formalism to deal with probabilistic modeling in the fields of Artificial Intelligence in general and Machine Learning in particular. They are mathematical tools that allow to carry out the main operations with probability distributions (estimation and inference) in an efficient way.

Goal: The objective in this line is to design new methods for learning probabilistic graphical models in general, and Bayesian networks in particular. These methods will be a hybrid between classical operations research techniques and metaheuristic methods. We will apply the developed techniques in the solution of real data analysis tasks.
Method: This area of research combines many mathematical methodologies. First of all are probability theory and statistics. The final objective is to learn a probability distribution. It also deals with graph theory as probabilistic graphical models are represented using graphs. A third topic used in the area is optimization. Most of the processes are based in optimizing a score, and therefore the area of combinatorial optimization, in particular is basic for the topic.

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