In the first unit, students will dive into computer vision, creating AI models representative of a variety of imaging datasets using Python code. By the end of this unit, students will have improved their conceptual understanding of AI and coding concepts. To achieve this, students will be exposed to, and successfully leverage, Google Vision API, machine learning basics, computer vision foundations, and neural networks.

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AI Creators 1

Unit 2 of this course focuses on Convolutional Neural Networks (CNN), the core technology behind almost all advanced computer vision systems. We will learn a couple of different approaches for running CNN systems using different data sets. Throughout the class, students will be doing hands-on projects to run these systems while learning the underlying technologies. By the end of this unit, students will have deepened their understanding and utilization of AI for computer vision and coding concepts. We will also introduce students to Stable Diffusion, the frontier of Generative AI art technology. Students will run their own stable diffusion system to create arts with more controls.

AI Creators 2

Unit 3 covers two key areas - ChatGPT and Computer Vision for medical imaging. We will first introduce students to ChatGPT, its underlying technologies, and then cover how to code ChatGPT to create your own Specialty ChatBot. The remaining classes will focus on two different projects that apply learnings on Computer Vision from the previous units to the real world problem of diagnosis based on medical imaging. In particular, we first model chest X-ray images using Fastai for CNN (pneumonia vs. normal vs. covid). We then model chest X-ray images by building CNN from scratch.