This course builds upon foundational Machine Learning knowledge and focuses on the transition from classical algorithms to modern Deep Learning systems. Students will learn how to design, train, and deploy neural networks for real-world applications, moving from traditional feature engineering to representation learning. The course emphasizes hands-on experimentation, real-world use cases, and the integration of modern AI tools such as Claude Code, OpenAI Codex, GitHub Copilot, and large language models (LLMs). Students will work across two learning tracks (low-code and high-code), allowing them to either focus on conceptual understanding and applied AI tools or develop advanced programming and model implementation skills.
By the end of the course, students will be able to build end-to-end deep learning solutions and understand how modern AI systems are developed and deployed in practice.
- Teacher: Rodolfo Capdevilla