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DTSTART;TZID=Europe/Brussels:20260928T000000
DTEND;TZID=Europe/Brussels:20261109T235959
DTSTAMP:20260918T083039Z
CREATED:20260918T082056Z
LAST-MODIFIED:20260918T083039Z
UID:10000114-1790553600-1794268799@unigreen-alliance.eu
SUMMARY:Applied Data Analysis with Machine Learning and LLMs for Staff
DESCRIPTION:A hands-on introduction to how modern AI tools work and how to apply them effectively across teaching\, research\, and administrative contexts.\n\n\n\n \n\n\n\n\nCourse format: Online\n\n\n\nType of program: E-Skill program\n\n\n\nTarget group: Academic and Non-academic Staff\n\n\n\nLanguage: English\n\n\n\nCost: Free\n\n\n\nAvailable places: 60\n\n\n\nTeaching hours: 27\n\n\n\nECTS: 3\n\n\n\n\nDeadline for admission is 16 September 2026 \n\n\n\nAudience\n\n\n\nThis course is designed for university professionals including academic staff\, researchers\, and administrative staff who want to gain a practical understanding of data analysis and Artificial Intelligence tools. It focuses on the use of tabular and textual data analysis techniques\, machine learning basics\, and Large Language Models to support everyday activities such as data processing\, information retrieval\, documentation\, and workflow automation. It also introduces the fundamentals of AI agents and their application to simple automated tasks. \n\n\n\nPre-requisites\n\n\n\n\nB2 or similar level of English is necessary.\n\n\n\nBasic knowledge of Python is required (e.g.\, understanding variables\, functions\, and basic scripting).\n\n\n\nBasic digital skills\, such as using documents and email\, are required.\n\n\n\nA Google account is required to access and run the practical activities on Google Colab notebooks.\n\n\n\n\n\n\n\n\n \n\n\n\nShort Description\n\n\n\nThis course provides a practical introduction to data analysis\, machine learning\, and Generative Artificial Intelligence\, with a particular focus on applications relevant to university technical and administrative staff. \n\n\n\nParticipants will learn how to acquire\, manipulate\, analyze\, and visualize tabular data using Python-based tools\, and how to develop machine learning workflows for classification\, regression\, and clustering tasks. The course will then introduce Large Language Models (LLMs) and Generative AI techniques for text analysis\, semantic search\, information extraction\, and workflow automation. \n\n\n\nThe course combines theoretical foundations with extensive hands-on activities based on Google Colab notebooks and real-world case studies. Particular attention will be devoted to practical problem solving\, enabling participants to apply AI and data-driven approaches to common institutional scenarios involving both structured and unstructured data. \n\n\n\nParticipants will: \n\n\n\n\nexplore AI agents and workflow automation solutions.\n\n\n\nlearn how to manipulate and transform tabular datasets using Pandas;\n\n\n\ncreate effective visualizations and perform exploratory data analysis;\n\n\n\nunderstand and apply machine learning techniques for classification\, regression\, and clustering;\n\n\n\ndevelop complete data analysis pipelines using Scikit-learn;\n\n\n\nunderstand the fundamental principles of Generative AI and Large Language Models;\n\n\n\napply semantic search\, text classification\, and information extraction techniques;\n\n\n\ndesign effective prompts for AI-assisted tasks;\n\n\n\nexplore AI agents and workflow automation solutions.\n\n\n\n\nContent\n\n\n\nModule 1 – Tabular Data Management with Pandas \n\n\n\n\nReading and writing data from different file formats;\n\n\n\nSeries and DataFrame structures;\n\n\n\nData selection\, indexing\, and filtering;\n\n\n\nData cleaning and transformation techniques;\n\n\n\nPractical exercises on real-world data manipulation tasks.\n\n\n\n\nModule 2 – Data Visualization \n\n\n\n\nPrinciples of data visualization;\n\n\n\nIntroduction to Matplotlib and Seaborn;\n\n\n\nCommon visualization techniques and plot types;\n\n\n\nUnivariate and multivariate data analysis;\n\n\n\nVisualization-driven exploratory data analysis.\n\n\n\n\nModule 3 – Machine Learning with Scikit-learn \n\n\n\n\nIntroduction to machine learning workflows;\n\n\n\nData preprocessing and feature transformations;\n\n\n\nScikit-learn estimators and pipelines;\n\n\n\nClassification methods and evaluation metrics;\n\n\n\nRegression methods and evaluation metrics;\n\n\n\nClustering techniques and dimensionality reduction (PCA);\n\n\n\nEnd-to-end machine learning pipelines.\n\n\n\n\nModule 4 – Text Analytics with Large Language Models \n\n\n\n\nIntroduction to Artificial Intelligence and Large Language Models;\n\n\n\nTransformer-based architectures;\n\n\n\nSemantic search and Retrieval-Augmented Generation concepts;\n\n\n\nText classification and clustering;\n\n\n\nPrompt engineering techniques;\n\n\n\nPractical applications on institutional documents and textual data.\n\n\n\n\nModule 5 – Agentic AI and Workflow Automation \n\n\n\n\nIntroduction to AI agents;\n\n\n\nTool integration and workflow orchestration;\n\n\n\nPractical examples of AI-assisted automation.\n\n\n\n\nLearning Outcomes\n\n\n\nAfter completing the course\, students will be able to: \n\n\n\n\nacquire\, manipulate\, and transform tabular datasets using Pandas;\n\n\n\nperform exploratory data analysis through appropriate visualizations;\n\n\n\ndevelop machine learning workflows for classification\, regression\, and clustering tasks;\n\n\n\nevaluate machine learning models using suitable performance metrics;\n\n\n\nbuild complete data processing and analysis pipelines using Scikit-learn;\n\n\n\nunderstand the fundamental principles of Generative AI and Large Language Models;\n\n\n\napply semantic search and text analytics techniques to institutional documents;\n\n\n\ndesign effective prompts for AI-assisted tasks;\n\n\n\nunderstand the principles of AI agents and workflow automation tools\n\n\n\n\nDuration\n\n\n\n\nSynchronous Activities (Lectures): 27 hours\n\n\n\nAsynchronous Activities (Exercises): 24 hours\n\n\n\nSelf-Learning: 24 hours\n\n\n\nTotal Student Workload: 75 hours\n\n\n\n\nSchedule: \n\n\n\n\nMonday\, September 28\, 2026 09:00-12:00\n\n\n\nThursday\, October 1\, 2026 09:00-11:00\n\n\n\nThursday\, October 8\, 2026 09:00-13:00\n\n\n\nMonday\, October 12\, 2026 09:00-12:00\n\n\n\nThursday\, October 15\, 2026 09:00-11:00\n\n\n\nMonday\, October 19\, 2026 09:00-12:00\n\n\n\nThursday\, October 22\, 2026 09:00-11:00\n\n\n\nMonday\, November 2\, 2026 09:00-12:00\n\n\n\nThursday\, November 5\, 2026 09:00-11:00\n\n\n\nMonday\, November 9\, 2026 09:00-12:00\n\n\n\n\nAssessment\n\n\n\nThe evaluation of the course is based on the completion of the practical assignments proposed during the course. \n\n\n\nParticipants are required to submit the assigned notebooks before the synchronous lecture in which the corresponding solutions will be discussed. Each notebook will be evaluated according to the completion of the assigned exercises and the correctness of the proposed solutions. \n\n\n\nThe course completion requirement is achieved by: \n\n\n\n\nattending at least 80% of the synchronous sessions.\n\n\n\nsubmitting all assigned notebooks;\n\n\n\nsuccessfully completing at least 70% of the proposed exercises across the practical activities.\n\n\n\n\nExercises will contribute equally to the final evaluation\, and partial completion of an exercise will be considered when assessing the overall completion level. The evaluation is intended to verify the participant’s ability to apply the concepts introduced during the course rather than to assess theoretical knowledge only. \n\n\n\nHow to Apply\n\n\n\nIf you are interested in this course\, register by clicking on the green bar (at the top and the botton of this page during the registration period).  \n\n\n\nYou will then be taken to the Boost platform to fill in your personal details and complete your pre-registration.  \n\n\n\nYou must use your institutional account from your home university to book this course\, If you do not have one\, you must request access credentials to this course Administrative Support team.  \n\n\n\nOnce you have them\, you will be able to log in to Boost and complete your registration.  \n\n\n\nFinally\, Administrative Support will confirm your registration and provide you with the login details for the online course through which you will be able to take this course. \n\n\n\nAdministrative Support: grighetti@unimore.it \n\n\n\n\nMore Info and Registration
URL:https://unigreen-alliance.eu/event/applied-data-analysis-with-machine-learning-and-llms-for-staff/
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DTSTART;TZID=Europe/Brussels:20261117T120000
DTEND;TZID=Europe/Brussels:20261117T130000
DTSTAMP:20260929T113104Z
CREATED:20260416T104814Z
LAST-MODIFIED:20260929T113104Z
UID:10000092-1794916800-1794920400@unigreen-alliance.eu
SUMMARY:Evaluating the Role of Evolutionary Processes in the Diversification of Lineages within Asteraceae from Tropical High-Elevation Ecosystems
DESCRIPTION:UNIgreen’s JRC4 – Biodiversity\, Forestry\, and Agroecology Webinar Series: Episode 9 \n\n\n\nThis event\, organized within the UNIgreen JRC4 Living Lab framework\, brings together researchers\, students\, and forest practitioners to discuss aliens and flames – when fire ecology meets invasion ecology. \n\n\n\nThematic\n\n\n\nThis seminar explores Tropical high elevation ecosystems are hotspots of species diversity and endemism\, with the majority of plant diversity sharing a recent origin on an evolutionary scale. The outstanding accumulation of plant diversity in these regions over a short period of time has often been explained by the availability of new habitats\, niche differentiation and the influence of geological and climatic changes in the connection and isolation of populations.  \n\n\n\nAsteraceae\, one of the most diverse plant groups in tropical high elevation hotspots\, is a great study system to characterise diversity patterns and determine the main evolutionary processes responsible for plant diversity in these regions. Additionally\, the combination of genomic\, ecological and morphological data with approaches in phylogenomics\, population genomics\, demographic modelling and lineage differentiation analyses open the door to the study of evolutionary processes at both the species and population levels. Three Asteraceae genera Oritrophium\, Senecio and Dendrosenecio were studied with the aim of extending the knowledge about current diversity and diversification processes behind recently evolved plant groups from tropical high elevation regions. Phylogenomic analyses revealed a recent evolution of the studied plant groups\, which evolved after tropical high elevation ecosystems emerged and subsequently diversified likely influenced by Pleistocene climatic oscillations.  \n\n\n\nPhylogenetic patterns were congruent with the geographic distribution of species\, but gene tree and cytonuclear discordances were found in all groups. However\, each group showed a different evolutionary history with links between habitat transitions and morphological traits that resembling a pattern of convergent adaptive evolution to habitat; ecologically-based speciation promoted by fine-scale habitat differences in mountainous regions; or lineage differentiation supporting heterogeneous divergence trajectories across the speciation continuum. \n\n\n\nWhen?\n\n\n\nOn Tuesday\, 17 November 2026\, join us for an online event\, from 12:00 to 13:00 CET with expert speaker and engaging discussions. \n\n\n\n\nREGISTRATION\n\n\n\n\nWho should join?\n\n\n\nResearchers\, students\, forest managers\, and anyone curious about forest ecology and sustainability. \n\n\n\nSpeaker\n\n\n\n\n\n\n\n\n\n\n\nJuan Manuel Gorospe\n\n\n\nPosdoctoral researcher at the Conservation Biology Research Group – RNM-344\, University of Almería \n\n\n\n\n\nHe obtained his PhD in Botany at Charles University in Prague\, Czechia. He has participated in projects and carried out research collaborations with academic institutions in Spain\, Czechia\, Norway\, Ecuador\, Peru\, Germany\, Austria and Chile. His research is centered in the study and conservation of plant diversity and has contributed to research fields such as landscape analysis\, conservation biology\, evolutionary biology\, experimental biology\, and biogeography. His scientific work employs a multidisciplinary perspective that combines genomic\, ecological and morphological approaches to understand which evolutionary processes gave rise to current diversity patterns.
URL:https://unigreen-alliance.eu/event/integrating-electrochemical-technologies-advanced-sustainable-water-treatment/
CATEGORIES:Webinar
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