# 💡 Hello World: Course Overview
```{note}
:class: margin
[Registration is live](https://learn.utoronto.ca/programs-courses/courses/4010-introduction-ai-discovery-using-self-driving-labs)! Course quizzes and grades are available at https://q.utoronto.ca/.
```
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.
```{margin}
Animated schematic diagram of the 'Hello World' demo: A microcontroller controls the LEDs and reads sensor data. The difference between the target color and the measured color is fed into an adaptive experimentation algorithm, and the process repeats itself.
```
```{image} ./images/clslab-light.gif
:align: center
:scale: 35 %
```
## 🔑 Prerequisites
```{include} ./hardware-note.md
```
For participants to complete this course within the expected timeframe (approx. 25 hours), at least beginner proficiency in Python programming is recommended. Those with advanced programming expertise will likely require a significantly shorter amount of time, whereas those with no prior programming experience may require 50 hours or more.
## 🎯 Learning Outcomes
- Define and explain key terms and principles of self-driving labs to demonstrate understanding
- Apply MQTT or similar frameworks to send commands and receive sensor data over WiFi
- Demonstrate the ability to use MongoDB to store and retrieve experiment configurations and results effectively
- Develop and implement software on a Raspberry Pi Pico W microcontroller to control device power and read sensor data accurately
- Modify a Bayesian optimization script using the Ax Platform to iteratively propose new experimental configurations
- Integrate the individual SDL components to orchestrate the full 'Hello World' workflow
## 🛠️ Competencies/Skills
- Basic self-driving lab literacy
- Microcontrollers and sensors
- Bayesian optimization
- Hardware/software communication
- Database management
- Workflow orchestration
## 🧩 Modules
The orientation modules are intended to be completed in under one hour in total. The modules after are intended to take approximately 3-4 hours each, assuming that the recommended prerequisites from above have been met.
```{list-table}
:header-rows: 1
* - Module Name
- Topics
- Learning Outcomes
* - 1.0 Orientation
- * Git
* GitHub
* Version control
* GitHub Classroom
* Codespaces
- * Describe the purpose of Git and GitHub
* Create a GitHub account and a repository
* Commit, push, and pull changes
* Run a unit test and fix a simple Python function
* Define continuous integration
* - 1.1 Run the demo
- * Database management
* Bayesian optimization
* Microcontrollers
- * Describe key terms and principles of self-driving labs
* Preview an end-to-end self-driving lab
* Upload software to a microcontroller
* - 1.2 Blink and read
- * Microcontrollers
* MicroPython
- * Write MicroPython scripts
* Use a microcontroller
* - 1.3 Bayesian optimization
- * Design of experiments
* Bayesian optimization
* Data visualization
- * Adapt a Bayesian optimization script to perform color-matching
* Compare Bayesian optimization with other search methods
* Visualize optimization efficiency
* - 1.4 Device communication
- * MQTT
* Broker/client
- * Send commands to a microcontroller
* Receive sensor data from a microcontroller
* - 1.5 Database Management
- * MongoDB
* JSON
* PyMongo
- * Set up a MongoDB account and database
* Upload data directly from microcontroller
* Extract and collate data from database
* - 1.6 Connecting the pieces
- * Systems design
- * Describe how individual components of an SDL can be integrated
* Perform system-level debugging and troubleshooting
* Conduct a full SDL experimental campaign
```
## ⚖️ Course Assessments and Grading Schema
Each student is required to complete various quizzes and GitHub Classroom assignments. The course is structured into an orientation module followed by several modules. The course is graded on a pass/fail basis with 70% as the threshold for passing. Here is the breakdown of the points for each part of the course:
- 🧭 Orientation Module: Worth 15 points.
- 📚 Modules 1-6: Each includes:
- 🧭 A guided notebook tutorial (ungraded)
- 📓 A knowledge check (graded, 5 points)
- 🛠️ A GitHub Classroom assignment (graded, 10 points*)
*The final module's GitHub Classroom assignment is worth 30 points.
Note that partial points are available on certain assignments.
## 👤 Course developer(s)
- Sterling Baird, PhD Materials Science and Engineering (Acceleration Consortium)