Ofertas de proyectos

Usted está aquí

Consulta ofertas de otros estudios y especialidades

Languages follow many statistical regularities called laws. Perhaps the most popular example is Zipf's law for word frequencies, that relates the frequency of a word with its rank, but other laws have been formulated, such as the law of abbreviation, the law of meaning-distribution, the meaning-frequency law,...and so on (Zipf 1949). About 15 years ago, a family of optimization models was introduced to shed light on the origins of Zipf's law for word frequencies (Ferrer-i-Cancho & Solé 2003, Ferrer-i-Cancho 2005). In that family, language is modelled as a bipartite graph where words connect to meanings and a cost function is defined based on the structure of that graph. A simple Monte Carlo algorithm was used to minimize the cost function while the structure of the graph was allowed to vary. Recently, it has been shown how these models shed light on how children learn words (Ferrer-i-Cancho 2017). The aim of this project is to investigate new versions of these models (e.g., Ferrer-i-Cancho & Vitevitch 2018) in two directions: (1) Providing an efficient implementation of the optimization algorithm. (2) Comparing the statistical properties of the model against the statistical properties of natural communication systems.

The master thesis consists of developing a framework for Group Recommender Systems and investigating the methods for generating recommendations to groups.

Ciencia de los Datos e Inteligencia Computacional Avanzada Ingeniería del Conocimiento y Aprendizaje Automático

Study and implement deep learning based recommendation methods such as Neural collaborative filtering or Neural matrix factorization (DeepFM), and applied in a real dataset of sales of an online retail shoe company (Camper) for recommending products to clients

This project aims to analyze the prediction capability of Optical Coherence Tomography Angiography (OCTA) images for Diabetes Mellitus (DM) and Diabetic Retinopathy (DR,) in a large high-quality image dataset from previous research projects carried out in the field of Ophthalmology (Fundacio¿ La Marato¿ TV3, Fondo Investigaciones Sanitarias, FIS). OCTA is a newly developed, non-invasive, retinal imaging technique that permits adequate delineation of the perifoveal vascular network. It allows the detection of paramacular areas of capillary non perfusion and/or enlargement of the foveal avascular zone (FAZ), representing an excellent tool for assessment of DR.

Ciencia de los Datos e Inteligencia Computacional Avanzada Temas Actuales y Práctica Profesional de la IA

Machine Learning with TinyML TinyML aims to do machine learning on microcontrolers. Microcontrolers are sometimes the only hardware choice when the power supply is limited, e.g. in for battery-operated applications. Just one application areas are "wildlife" observation, such as: https://mybirdbuddy.com/ https://opencollar.io/ Arduino Uno and Mega boards are well known for all kinds of hobbyist microcontroler projects, but there is another kind of more powerful 32 bit microcontrolers and develpment boards, which are able to run machine learning applications. To get the first information on this topic you can have a look at TensorFlow Lite for Microcontrollers: https://www.tensorflow.org/lite/microcontrollers We have a couple of the mentioned boards like the Arduino Nano 33 BLE Sense, STM32F746 Discovery kit and Espressif ESP32 microcontrolers which can be used for this project. The project will in a first phase explore with practically example applications the topic and do some reading to get a basic understanding of the background. Then the second phase can be shaped accroding to the interest: The project could either develop and deploy a specific applications of interest or focus on analyzing and experimenting a specific step of the TinyML machine learning (ML) pipeline which starts at data acquisition and building a machine learning model until doing the deployment and evaluating the application. Other ideas can be suggested, also the integrating an ML-component running on a microcontroler with network connectivity into a distributed application can be discussed. You can find several TinyML examples in Tensorflow, medium or towardsdatascience webs, which people have already tried with code in github repositories e.g. https://codelabs.developers.google.com/codelabs/ai-magicwand/#0 https://www.digikey.es/en/maker/projects/intro-to-tinyml-part-1-training-a-model-for-arduino-in-tensorflow/8f1fc8c0b83d417ab521c48864d2a8ec https://towardsdatascience.com/tensorflow-meet-the-esp32-3ac36d7f32c7

Interacción Persona-Máquina Ciencia de los Datos e Inteligencia Computacional Avanzada Ingeniería del Conocimiento y Aprendizaje Automático

In recent years the volume of information available electronically has increased exponentially, coining the term Big Data to refer to this phenomenon. The medical domain is an area in which the number of documents generated by the centers for patient primary care constantly increases. However, a bottleneck is generated because processing these documents requires specialized personnel craftly performing tasks. In the framework of TADIAMED research project, we are developing a set of processors that allow automatic analysis of medical texts taking into account criteria of robustness, high precision and coverage. In particular, this thesis would aim at the acquisition of patterns of clinical behavior from medical records, represented as semantic graphs using Neo4J database.

The goal of this project is to analyze and mitigate bias in collaborative filtering recommender systems.

Consulta ofertas de otros estudios y especialidades