María Martínez-García

María Martínez-García

Postdoctoral researcher at the Probabilistic Machine Learning Group, under the supervision of Prof. Isabel Valera. Saarland University.

Teaching and supervision

Advanced Time Series Analysis: From Probabilistic to Foundational Models

Seminar, Saarland University, 2025

Time series analysis studies data that change as a function of time, such as stock market prices, weather patterns, or household electricity consumption. This seminar covers advanced techniques for analyzing time series, starting with probabilistic methods and progressing to state-of-the-art deep learning approaches, including neural architectures and foundation models. We will also explore connections between time series and other modalities, such as text and images/videos, to offer a comprehensive view of the field. The aim is for students to critically assess existing methods, understand their strengths and limitations, and identify potential directions for future research.

Deep Probabilistic Generative Models

Seminar, Saarland University, 2025

With the development of neural networks and increased computational power, deep generative modeling has emerged as one of the leading directions in AI. We are shifting from traditional discriminative tasks (such as classification, segmentation, or clustering), which focus on modeling conditional distributions, to a more comprehensive framework aimed at modeling the joint distribution of the data itself. Discriminative models alone can be insufficient for robust decision-making and the development of intelligent systems, as it is also necessary to understand the underlying data-generating process and be able to express uncertainty about the environment.

Teacher Assistant in AI in Health

Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2023

The objectives of the matter are to present the possibilities and limitations of the application of AI in the field of health, to present problems in the field of health in which AI techniques can be applied and develop the capacity to apply AI techniques in some health problems.

Teacher Assistant in Neural Networks

Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2022

The fundamental objective is that the student learns to design decision machines based on neural networks for basic learning problems in tabular and multimedia data, paying special attention to regularization and validation techniques. Likewise, the student will learn to use automatic differentiation software packages for model training and experimental simulation.

Teacher Assistant in Deep Learning

Master Course, Universidad Carlos III de Madrid, Master in Applied Artificial Intelligence, 2022

The fundamental objective of this subject is for the student to know and learn to use learning schemes based on advanced neural networks, with special emphasis on computer vision applications, treatment of temporal signals and text, and the adjustment of probabilistic models for the generation of artificial data.