Gamifying the Classroom for the Acquisition of Skills Associated with Machine Learning: A Two-Year Case Study

Machine learning (ML) is the field of science that combines knowledge from artificial intelligence, statistics and mathematics intending to give computers the ability to learn from data without being explicitly programmed to do so. It falls under the umbrella of Data Science and is usually developed by Computer Engineers becoming what is known as Data Scientists. Developing the necessary competences in this field is not a trivial task, and applying innovative methodologies such as gamification can smooth the initial learning curve. In this context, communities offering platforms for open competitions such as Kaggle can be used as a motivating element. The main objective of this work is to gamify the classroom with the idea of providing students with valuable hands-on experience by means of addressing a real problem, as well as the possibility to cooperate and compete simultaneously to acquire ML competences. The innovative teaching experience carried out during two years meant a great motivation, an improvement of the learning capacity and a continuous recycling of knowledge to which Computer Engineers are faced to

Empowering the Data Scientist professional profile through competition dynamics

La Ciencia de Datos es el área que comprende el desarrollo de métodos científicos, procesos y sistemas para extraer conocimiento a partir de datos recopilados previamente, con el objetivo de analizar los procedimientos llevados a cabo actualmente. El perfil profesional asociado a este campo es el del Científico de Datos, generalmente llevado a cabo por Ingenieros Informáticos gracias a que las aptitudes y competencias adquiridas durante su formación se ajustan perfectamente a lo requerido en este puesto laboral. Debido a la necesidad de formación de nuevos Científicos de Datos, entre otros fines, surgen plataformas en las que éstos pueden adquirir una amplia experiencia, como es el caso de Kaggle. El principal objetivo de esta experiencia docente es proporcionar al alumnado una experiencia práctica con un problema real, así como la posibilidad de cooperar y competir al mismo tiempo. Así, la adquisición y el desarrollo de las competencias necesarias en Ciencia de Datos se realiza en un entorno altamente motivador. La realización de actividades relacionadas con este perfil ha tenido una repercusión directa sobre el alumnado, siendo fundamental la motivación, la capacidad de aprendizaje y el reciclaje continuo de conocimientos a los que se someten los Ingenieros Informáticos.Data Science is the area that comprises the development of scientific methods, processes, and systems for extracting knowledge from previously collected data, aiming to analyse the procedures being carried out currently. The professional profile associated with this field is the Data Scientist, generally carried out by Computer Engineers as the skills and competencies acquired during their training are perfectly suited to what this job requires. Due to the need for training new Data Scientists, among other goals, there are different emerging platforms where they can acquire extensive experience, such as Kaggle. The main objective of this teaching experience is to provide students with practical experience on a real problem, as well as the possibility of cooperating and competing at the same time. Thus, the acquisition and development of the necessary competencies in Data Science are carried out in a highly motivating environment. The development of activities related to this profile has had a direct impact on the students, being fundamental the motivation, the learning capacity and the continuous recycling of knowledge to which Computer Engineers are subjected

Presentación de "Science for policy"

Datos técnicos: 187 minutos, color, español. Ficha técnica: Gabinete de Presidencia CSIC y Departamento de Comunicación. Emitido en directo el 28 jun 2023El Consejo Superior de Investigaciones Científicas (CSIC) presenta este miércoles, 28 de junio, en su sede central en Madrid, los informes Ciencia para las Políticas Públicas (Science For Policy). Elaborados por equipos de investigadores del CSIC, tienen el objetivo de servir de puente entre los centros de investigación y los decisores políticos para contribuir a la definición de políticas públicas basadas en la evidencia científica.Peer reviewe

A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks

Abstract Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance

Ordinal classification of the affectation level of 3D-images in Parkinson diseases

Parkinson's disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of [Formula: see text]I-ioflupane, considering a binary classification problem (absence or existence of Parkinson's disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.This research has been partially supported by the “Ministerio de Economía, Industria y Competitividad” of Spain (Ref. TIN2017-85887-C2-1-P) and the “Fondo Europeo de Desarrollo Regional (FEDER) y de la Consejería de Economía, Conocimiento, Empresas y Universidad” of the “Junta de Andalucía” (Spain) (Ref. UCO-1261651).Ye

Identification of the initial molecular changes in response to circulating angiogenic cells-mediated therapy in critical limb ischemia

Background: Critical limb ischemia (CLI) constitutes the most aggressive form of peripheral arterial occlusive disease, characterized by the blockade of arteries supplying blood to the lower extremities, significantly diminishing oxygen and nutrient supply. CLI patients usually undergo amputation of fingers, feet, or extremities, with a high risk of mortality due to associated comorbidities. Circulating angiogenic cells (CACs), also known as early endothelial progenitor cells, constitute promising candidates for cell therapy in CLI due to their assigned vascular regenerative properties. Preclinical and clinical assays with CACs have shown promising results. A better understanding of how these cells participate in vascular regeneration would significantly help to potentiate their role in revascularization. Herein, we analyzed the initial molecular mechanisms triggered by human CACs after being administered to a murine model of CLI, in order to understand how these cells promote angiogenesis within the ischemic tissues. Methods: Balb-c nude mice (n:24) were distributed in four different groups: healthy controls (C, n:4), shams (SH, n:4), and ischemic mice (after femoral ligation) that received either 50 μl physiological serum (SC, n:8) or 5 × 105 human CACs (SE, n:8). Ischemic mice were sacrificed on days 2 and 4 (n:4/group/day), and immunohistochemistry assays and qPCR amplification of Alu-human-specific sequences were carried out for cell detection and vascular density measurements. Additionally, a label-free MS-based quantitative approach was performed to identify protein changes related. Results: Administration of CACs induced in the ischemic tissues an increase in the number of blood vessels as well as the diameter size compared to ischemic, non-treated mice, although the number of CACs decreased within time. The initial protein changes taking place in response to ischemia and more importantly, right after administration of CACs to CLI mice, are shown. Conclusions: Our results indicate that CACs migrate to the injured area; moreover, they trigger protein changes correlated with cell migration, cell death, angiogenesis, and arteriogenesis in the host. These changes indicate that CACs promote from the beginning an increase in the number of vessels as well as the development of an appropriate vascular network.This study was supported by the Institute of Health Carlos III, ISCIII (PI16-00784) and the “Programa Operativo de Andalucia FEDER, Iniciativa Territorial Integrada ITI 2014-2020 Consejeria de Salud, Junta de Andalucia (PI0026-2017).Ye