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This course is designed as a week-long tutorial to engage with ubiquitous devices in the domain of smart environments and how to use machine learning to build smart devices. Here, we use an [[Arduino_Nano_RP2040_Connect|Arduino Nano RP2040 Connect]]. | This course is designed as a week-long tutorial to engage with ubiquitous devices in the domain of smart environments and how to use machine learning to build smart devices. Here, we use an [[Arduino_Nano_RP2040_Connect|Arduino Nano RP2040 Connect]]. | ||
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= Schedule = | = Schedule = | ||
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== Day 3 == | == Day 3 == | ||
− | * 10:00- | + | * 10:00-12:00 Lecture: Introduction to Jupyter Notebooks, training an ML model based on a given data and the self-recorded data set on the PC (* using Google Python Notebooks or personal installation) |
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* 12:00-13:00 Lunch break | * 12:00-13:00 Lunch break | ||
− | * 13:00-15: | + | * 13:00-15:00 Hands-On: [[UBISS2024#Task 6: Getting Started with Jupyter Notebook|Task 6: Getting Started with Jupyter Notebook]] Installing Jupyter Notebook for Micropython, controlling LED, reading data, storing data |
+ | * 15:00-15:30 Hands-On: Project specification, Ideation on Project Ideas | ||
* 15:30-16:00 Coffee break | * 15:30-16:00 Coffee break | ||
− | * 16:00-16:30 Hands-On: Presentation | + | * 16:00-16:30 Hands-On: Presentation and discussion of project ideas, group forming (groups of 2 or 3) |
− | * 16: | + | * 16:30-17:00 Lecture: Introduction to ML Libraries (everywhereML) |
+ | * 17:00-18:00 Hands-On: [[UBISS2024#Task 7: Deploy Machine Learning Models|Task 7: Deploy Machine Learning Models]] Implementing a basic model using everywhereML | ||
== Day 4 == | == Day 4 == | ||
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* 15:00-15:30 Hands-On: stand-up meeting on project progress | * 15:00-15:30 Hands-On: stand-up meeting on project progress | ||
* 15:30-16:00 Coffee break | * 15:30-16:00 Coffee break | ||
− | * 16:00 | + | * 16:00-17:30 Hands-On: project work |
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* 17:30-18:00 Lecture: How to Evaluate ML Solutions (talk and discussion) | * 17:30-18:00 Lecture: How to Evaluate ML Solutions (talk and discussion) | ||
== Day 5 == | == Day 5 == | ||
* 10:00-10:30 Hands-On: stand-up meeting — project challenges and solutions | * 10:00-10:30 Hands-On: stand-up meeting — project challenges and solutions | ||
− | * 10:30-11:30 Hands-On: project work | + | * 10:30-11:30 Hands-On: project work |
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* 11:30-12:00 Lecture: Pitfalls and Challenges in Developing ML/AI for IoT | * 11:30-12:00 Lecture: Pitfalls and Challenges in Developing ML/AI for IoT | ||
* 12:00-13:00 Lunch break | * 12:00-13:00 Lunch break | ||
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* 13:15-18:15: Workshop Result Presentations | * 13:15-18:15: Workshop Result Presentations | ||
* 18:30-18:50: Debriefing | * 18:30-18:50: Debriefing | ||
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= Tasks = | = Tasks = | ||
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* Install the Arduino Lab for MicroPython development environment, https://labs.arduino.cc/en/labs/micropython | * Install the Arduino Lab for MicroPython development environment, https://labs.arduino.cc/en/labs/micropython | ||
* Task 1.1: Make the orange LED (pin 6) blink using micro python https://docs.arduino.cc/micropython/basics/digital-analog-pins/ | * Task 1.1: Make the orange LED (pin 6) blink using micro python https://docs.arduino.cc/micropython/basics/digital-analog-pins/ | ||
− | * Task 1.2: Connect an external RGB LED (pin | + | * Task 1.2: Connect an external RGB LED (pin 2, 3, 4) and control it (on, off, mix color, brightness), https://www.sketching-with-hardware.org/wiki/RGB_LED |
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=== Solution Task 1.1: LED Blinking === | === Solution Task 1.1: LED Blinking === | ||
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# RGB LED connected to the RP2040 | # RGB LED connected to the RP2040 | ||
− | ledG = Pin( | + | ledG = Pin(2, Pin.OUT) |
− | ledR = Pin( | + | ledR = Pin(3, Pin.OUT) |
− | ledB = Pin( | + | ledB = Pin(4, Pin.OUT) |
print("start") | print("start") | ||
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ledB.on() | ledB.on() | ||
time.sleep_ms(250) | time.sleep_ms(250) | ||
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</syntaxhighlight> | </syntaxhighlight> | ||
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== Task 7: Deploy Machine Learning Models == | == Task 7: Deploy Machine Learning Models == | ||
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in Task 2.2. For this, install everywhereml: | We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in Task 2.2. For this, install everywhereml: | ||
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<syntaxhighlight lang="Bash"> | <syntaxhighlight lang="Bash"> | ||
− | pip3 install -U | + | pip3 install -U everywhere |
</syntaxhighlight> | </syntaxhighlight> | ||
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C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook | C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook | ||
− | [[Category: | + | [[Category:Course]] |
[[Category:UBISS2024]] | [[Category:UBISS2024]] | ||
[[Category:Arduino Nano RP2040 Connect]] | [[Category:Arduino Nano RP2040 Connect]] | ||
[[Category:MicroPython]] | [[Category:MicroPython]] |