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Learn the foundational concepts, starting with the Perceptron algorithm, its biological inspiration, and limitations (e.g., the XOR problem).

Python fundamentals, setting up the environment, and basic syntax essential for data tasks.

This session explores the benefits and setup of running Large Language Models locally, covering tools like Ollama and LM Studio, model downloading, command-line and API usage, GUI interaction, local inference servers, and basic hardware considerations.

Mastering data manipulation and cleaning using Python's core data science libraries.

Join us as we explore the convergence of GIS/Remote Sensing with AI and how this will lower technical barriers while being a force multiplier in geographic data analysis.

A session focused on fostering thriving diverse teams through humble, authentic leadership, positive mindset, and strong team dynamics to enable collaboration and success from any position in the organization.

This session introduces Autoencoders for unsupervised learning, covering their encoder-bottleneck-decoder architecture, reconstruction loss, key variants like Denoising and Variational Autoencoders, and applications such as anomaly detection and data compression.

Creating impactful data visualizations to uncover patterns and communicate findings.

This session demonstrates autonomous geospatial analysis using AI tools: executing QGIS commands with Claude and generating Earth Engine code with Google's Gemini.

This session explores AI VERDE (or a similar platform) as a hub for accessing curated open-source LLMs, covering its goals, interface navigation, model selection, and unique features for research and education like integrated datasets and collaborative tools.