Curriculum

Machine Learning
Microcontrollers & IOT

Machine Learning Microcontrollers & IOT

About the Program

In the Machine Learning for Microcontrollers & IoT course, students will learn how to integrate machine learning models into resource-constrained devices like microcontrollers, sensors, and IoT systems. The course focuses on enabling IoT devices to make intelligent decisions locally, using on-device machine learning algorithms for real-time data processing, predictive analytics, anomaly detection, and automation.

With the increasing demand for smart devices in industries like agriculture, healthcare, smart homes, and manufacturing, this course equips students with the knowledge to leverage the power of machine learning on embedded systems. By the end of the course, students will be able to deploy machine learning models directly onto microcontrollers and IoT devices to make data-driven decisions in real-world applications such as predictive maintenance, environmental monitoring, and automated systems.

Through hands-on projects, students will learn to work with popular microcontroller platforms (e.g., Arduino, ESP32, Raspberry Pi) and machine learning frameworks optimized for embedded devices (such as TensorFlow Lite for Microcontrollers, Edge Impulse, and others).

Course Objectives:

  1. Understand the basics of microcontrollers and IoT systems:
    • Gain knowledge of microcontroller hardware and IoT architecture.
    • Understand how sensors, actuators, and communication modules work within an IoT ecosystem.
  2. Learn how to implement machine learning on embedded devices:
    • Understand the constraints and limitations of microcontrollers and IoT devices.
    • Learn how to deploy lightweight machine learning models on microcontrollers for real-time decision-making.
  3. Integrate machine learning models into IoT applications:
    • Develop applications for predictive maintenance, anomaly detection, sensor data classification, and other IoT-specific use cases.
    • Learn how to use cloud services for training models and deploying them to edge devices.
  4. Optimize models for embedded systems:
    • Use model compression and optimization techniques (such as quantization, pruning, etc.) to ensure models run efficiently on microcontrollers.
    • Understand how to balance model accuracy with the limited resources (memory, processing power) available on microcontrollers.
  5. Work with machine learning frameworks and tools for embedded systems:
    • Gain practical experience using TensorFlow Lite for Microcontrollers, Edge Impulse, and other tools designed for machine learning on IoT devices.
Requirements

At least a core i5 computer, 8GB RAM. Prior experience with C++ or Python and Arduino is required.

Student to Teacher Ratio of 10:1
Machine Learning Microcontrollers & IOT

Curriculum

1. Introduction to Machine Learning on Microcontrollers & IoT
  1. Overview of microcontrollers and IoT systems.
  2. The role of machine learning in IoT and edge computing.
  3. Introduction to TensorFlow Lite for Microcontrollers, Edge Impulse, and other frameworks.
  4. Exploring real-world applications of machine learning in IoT (smart homes, agriculture, healthcare, etc.).
  1. Understanding microcontroller hardware (e.g., Arduino, ESP32, Raspberry Pi).
  2. How sensors, actuators, and communication modules work in IoT.
  3. Basics of networking and communication protocols (MQTT, HTTP, etc.).
  4. Hands-on: Setting up a simple IoT sensor network using an Arduino or Raspberry Pi.
  1. Understanding machine learning concepts: supervised, unsupervised learning, and reinforcement learning.
  2. The challenges of running machine learning on embedded devices: memory, storage, and processing power.
  3. Overview of edge computing and its importance in IoT.
  4. Hands-on: Setting up a machine learning project for microcontrollers.
  1. How to collect and preprocess sensor data for machine learning.
  2. Techniques for handling noisy, incomplete, and large-scale sensor data.
  3. Exploring real-time data streaming and storage for IoT applications.
  4. Hands-on: Collecting data from an IoT sensor (temperature, humidity, etc.) and preprocessing it for machine learning.
  1. Choosing the right machine learning algorithm for IoT devices.
  2. Training models on the cloud using IoT data.
  3. Model selection and evaluation techniques for IoT applications.
  4. Hands-on: Building and training a simple model for IoT data classification (e.g., temperature anomaly detection).
  1. Model compression, quantization, and pruning techniques for smaller models.
  2. Optimizing machine learning models for power efficiency, memory usage, and speed.
  3. Techniques for evaluating the performance of models on microcontrollers.
  4. Hands-on: Optimizing a trained machine learning model for deployment on a microcontroller (e.g., using TensorFlow Lite).
  1. Steps for deploying machine learning models to microcontrollers.
  2. Using TensorFlow Lite for Microcontrollers or other frameworks to run models on IoT devices.
  3. How to interface microcontrollers with sensors and actuators for real-time decision-making.
  4. Hands-on: Deploying a machine learning model to an Arduino or Raspberry Pi.
  1. Practical examples of IoT applications with machine learning: predictive maintenance, environmental monitoring, anomaly detection, etc.
  2. Integrating machine learning models into an IoT system that reacts to real-time data (e.g., turning on a fan when temperature exceeds threshold).
  3. Hands-on: Build a real-time IoT application with machine learning (e.g., temperature anomaly detection and automated control).
  1. Introduction to deep learning and its application on IoT devices.
  2. Using reinforcement learning for autonomous IoT systems.
  3. Exploring edge AI and the future of machine learning in IoT.
  4. Hands-on: Implementing an advanced ML model (e.g., reinforcement learning for smart devices).
  1. Objective: Students will work on a capstone project that integrates machine learning with IoT devices to solve a real-world problem.
  2. Project Ideas:
    • Build a smart irrigation system using machine learning to predict soil moisture and optimize watering.
    • Create a predictive maintenance system for industrial machines using sensor data.
    • Develop a smart energy management system that uses IoT sensors to optimize energy usage in a building.
  3. Deliverables:
    • A working IoT device (e.g., Arduino, Raspberry Pi) integrated with a machine learning model.
    • A complete application with real-time data processing, predictions, and device automation.
    • Code and documentation outlining the setup, deployment, and usage of the system.


Program Expectations

By the end of this course, here’s what you’ll be able to achieve:

  1. Be able to collect, preprocess, and use IoT data for machine learning applications.
  2. Understand how to deploy optimized machine learning models to microcontrollers and IoT devices.
  3. Be capable of building intelligent IoT systems that make real-time decisions based on sensor data.
  4. Gain hands-on experience with popular machine learning frameworks for embedded systems, including TensorFlow Lite for Microcontrollers and Edge Impulse.
  5. Develop the skills to create innovative IoT solutions for industries like smart homes, agriculture, and industrial automation.

This course is ideal for students interested in bringing machine learning to the edge, enabling IoT devices to make intelligent, data-driven decisions without the need for cloud processing.

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