# Introduction to AI for Discovery using Self-driving Labs Star Follow @AccelerationConsortium Follow @sgbaird Issue Get Help ```{note} :class: margin If you haven't already, watch [the welcome video](https://www.youtube.com/embed/videoseries?si=Dt-C256m6yvzfnYT&list=PLKFxDV1Aoxg6dBasfkbG-zFQoy2RDOidX&t=623s) below to learn about the course and its structure. ``` In this course, you will build a minimal working example for a self-driving lab, using dimmable LEDs and a light sensor to perform a color-matching task. This introduction will help you implement microcontroller programming via a Pico W, Bayesian optimization via the Ax Platform, device communication via MQTT, and database integration via MongoDB. Finally, you will piece together the individual components to complete your self-driving lab. This introductory course will prepare you for deeper dives in data science, robotics, software development, and system design in later microcourses. ::::{grid} 1 2 3 3 :::{grid-item-card} Overview View prerequisites, learning outcomes, and topics. +++ ```{button-ref} overview :color: info :expand: :click-parent: Explore ``` ::: :::{grid-item-card} Registration Register to gain access to quizzes and assignments. +++ ```{button-link} https://learn.utoronto.ca/programs-courses/courses/4010-introduction-ai-discovery-using-self-driving-labs :color: info :expand: :click-parent: Register {octicon}`link-external;1em` ``` ::: :::{grid-item-card} Course Content Begin working through modules, one-by-one. +++ ```{button-ref} 1.0-orientation :color: info :expand: :click-parent: Begin ``` ::: :::: ```{toctree} :hidden: 🗺️ Overview 🎓 Register 🧩 1.0 Orientation <1.0-orientation.md> 🧩 1.1 Run the demo <1.1-running-the-demo.md> 🧩 1.2 Blink and read <1.2-blink-and-read.md> 🧩 1.3 Bayesian optimization <1.3-bayesian-optimization.ipynb> 🧩 1.4 Device communication <1.4-hardware-software-communication.md> 🧩 1.5 Database Management <1.5-data-logging.md> 🧩 1.6 Connecting the pieces <1.6-connecting-the-pieces.md> ```