Modern Computer Vision: From Geometry to Generative Models

Computer vision is transforming how machines interact with the physical world, powering everything from autonomous navigation to generative art. To build or work with these intelligent systems, you need a solid grasp of both classical spatial geometry and modern deep learning architectures. This text-only course guides you step-by-step through the core concepts, helping you transition from basic image processing to cutting-edge visual AI. Through clear written explanations and structured code snippets, you will build a strong conceptual framework for interpreting 2D and 3D data. You will explore how modern algorithms reconstruct environments, generate realistic imagery, and assist physical agents in decision-making. What you'll learn: - Understand foundational image formation, coordinate systems, and classical multi-view geometry. - Explore neural scene representations and the mechanics behind differentiable rendering. - Analyze generative modeling concepts, including diffusion models and conditional probabilistic frameworks. - Study representation learning through contrastive and self-supervised learning techniques. - Examine how embodied agents use computer vision for spatial planning and robotic control. - Practice reading and interpreting core computer vision algorithms using clean, modern Python structures. The course begins with essential terminology and geometric baselines before moving into advanced neural representations, generative pipelines, and robotic applications. This logical flow ensures you build a complete, end-to-end understanding of the field. This course is designed for beginner developers, data scientists, and technology enthusiasts eager to explore visual AI. No prior experience in image processing is required. Start reading today to unlock the fundamentals of modern visual computing.