Embedded AI Systems: Bridging Intelligence and Hardware.

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This course provides IT students with a foundational and practical understanding of Embedded Artificial Intelligence (AI) systems. Students will learn the core concepts of AI and Machine Learning, their application within resource-constrained embedded environments, and the critical considerations for designing, developing, and deploying intelligent edge devices. Through hands-on examples and theoretical insights, students will gain the skills necessary to contribute to the rapidly growing field of AIoT (Artificial Intelligence of Things)

Curriculum

3 Sections
13 Lessons
2 Quizzes
6h Duration

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Introduction to Embedded Systems & AI Fundamentals

3 Lessons1 Quiz

Lesson 1.1: What are Embedded Systems?
Lesson 1.2: Introduction to Artificial Intelligence & Machine Learning
Lesson 1.3: Why Embedded AI? The AIoT Revolution
Embedded AI Foundations Assessment

Embedded Hardware for AI

7 Lessons1 Quiz

Lesson 2.1 Microcontrollers vs. AI Accelerators
Lesson 2.1.2: Traditional MCUs for AI
Lesson 2.1.3: Introduction to AI Accelerators
Lesson 2.1.4: Purpose and Role in Embedded AI
Lesson 2.1.5: Trade-offs Between MCUs and AI Accelerators
Lesson 2.2: Sensors and Data Acquisition for Embedded AI
Lesson 2.3: Memory Architectures and Storage Considerations
Hardware & Sensing Assessment

Software Development for Embedded AI

2 Lessons

Lesson 3.1: Introduction to TinyML and Frameworks
Lesson 3.2: Embedded C/C++ Programming for AI

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Last Updated: August 11, 2025

This course includes

This course is meticulously designed to equip IT students with a powerful blend of theoretical knowledge and practical skills in the dynamic field of Embedded Artificial Intelligence.
- Foundational Mastery: You'll gain a solid understanding of both embedded systems (microcontrollers, sensors, actuators, and their limitations) and the core concepts of Artificial Intelligence, Machine Learning, and Deep Learning. This includes distinguishing between edge and cloud AI paradigms.
- Hardware Expertise: Develop skills in selecting and optimizing hardware for AI applications, including traditional MCUs and specialized AI accelerators like NPUs, TPUs, and FPGAs. Learn about sensor integration and memory management, crucial for resource-constrained environments.
- Software Proficiency: Dive into TinyML frameworks (TensorFlow Lite Micro, Edge Impulse) and master embedded C/C++ programming for AI. You'll learn to optimize code for optimal performance and memory usage, and effectively deploy pre-trained models on edge devices.
- Practical Application: Explore real-world case studies in areas such as computer vision (e.g., smart surveillance, gesture recognition), audio processing (e.g., keyword spotting, anomaly detection), and predictive maintenance. This section emphasizes hands-on implementation and problem-solving.
- Future-Ready Skills: Understand crucial advanced topics, including power management, security, and privacy in embedded AI. Get an introduction to cutting-edge concepts like Edge Learning and Federated Learning, preparing you for future trends and diverse career paths in the AIoT space, such as Embedded AI Engineer or TinyML Developer.

Embedded AI Systems: Bridging Intelligence and Hardware.

Curriculum

Introduction to Embedded Systems & AI Fundamentals

Embedded Hardware for AI

Software Development for Embedded AI

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