The Indian Institute of Science (IISc) in Bangalore, one of India's premier research institutions, offers an innovative and intensive course in Embedded Machine Learning. This cutting-edge programme bridges the gap between artificial intelligence and embedded systems, focusing on the design and implementation of efficient machine learning models for resource-constrained devices. As the Internet of Things (IoT) and edge computing continue to revolutionise various industries, the demand for professionals skilled in embedded ML is skyrocketing. This course presents an excellent opportunity for students, academics, and industry professionals to gain expertise in this rapidly evolving field. 

Course Overview 

The Embedded Machine Learning course at IISc is a three-day intensive programme scheduled from July 29 to July 31, 2024. Classes run from 9:30 AM to 5:30 PM each day, providing a comprehensive learning experience through in-person lectures and hands-on laboratory sessions. The course is conducted on the IISc campus, allowing participants to immerse themselves in a world-class academic environment. 

Faculty Coordinator

The course is led by Dr. Pandarasamy Arjunan, an Assistant Professor at the Robert Bosch Centre for Cyber Physical Systems. Dr. Arjunan's expertise in the field ensures that participants receive top-notch instruction and guidance throughout the programme. 

Entry Requirements 

The course is open to a diverse range of participants, including: 

1. B.E/B.Tech graduates from any discipline 

2. MCA degree holders 

3. M.Sc graduates in Computer Science, Information Technology, or Electronics 

While the course welcomes participants from various backgrounds, certain prerequisites are essential to ensure that all attendees can fully benefit from the programme: 

1. Programming Skills: Proficiency in C programming, particularly with Arduino, and Python is required. These languages form the foundation for many embedded systems and machine learning applications. 

2. Embedded Systems Knowledge: A basic understanding of embedded systems and microcontrollers is necessary. This includes familiarity with concepts such as real-time operating systems, interrupt handling, and hardware interfaces. 

3. Machine Learning Fundamentals: While not mandatory, a basic knowledge of machine learning and deep learning concepts is advantageous. Participants with this background will find it easier to grasp the more advanced topics covered in the course. 

These prerequisites ensure that participants can engage effectively with the course material and practical sessions. For those who may need to brush up on these skills, IISc recommends reviewing relevant online resources or introductory courses before attending. 

The course fees are structured to accommodate both academic and industry participants:

1. Students and Academicians: Rs. 20,000 + 18% GST
2. Industry and R&D candidates: Rs. 30,000 + 18% GST 

This tiered pricing structure ensures that the course remains accessible to students and academic professionals while also reflecting its value for industry participants. The fees cover all course materials, access to laboratory equipment, and instruction from expert faculty. 

It's important to note that the course is conducted offline, with all classes held on the IISc campus. Accommodation is not provided as part of the course fee, so out-of-town participants will need to arrange their own lodging. 

Detailed Course Content 

The Embedded Machine Learning course is meticulously designed to provide a comprehensive understanding of the field over three intensive days. Each day combines theoretical lectures in the morning with practical laboratory sessions in the afternoon, ensuring a balanced approach to learning. 

Day 1: Introduction to Embedded Machine Learning 

The first day lays the foundation for the course, introducing key concepts and technologies: 

1. Overview of IoT, Embedded Systems, and Microcontrollers 

   - Understanding the IoT ecosystem 

   - Fundamentals of embedded systems architecture 

   - Introduction to microcontroller technologies and their constraints 

2. Machine Learning Basics 

   - Core concepts of machine learning 

   - Supervised, unsupervised, and reinforcement learning paradigms 

3. Machine Learning vs. Embedded Machine Learning 

   - Unique challenges of implementing ML on resource-constrained devices 

   - Trade-offs between model complexity and device capabilities 

4. Basics of Deep Learning 

   - Neural network architectures 

   - Activation functions and backpropagation 

5. Deep Learning for Computer Vision 

   - Convolutional Neural Networks (CNNs) 

   - Image classification techniques 

   - Object detection algorithms 

Laboratory Session: 

- Arduino programming with MicroPython 

- Training and testing computer vision models 

 Day 2: ML Model Compression and Optimisation 

The second day focuses on techniques to make ML models more efficient for embedded systems: 

1. Overview of Model Optimisation Techniques 

   - Importance of model optimisation for embedded systems 

   - Different approaches to reducing model size and complexity 

2. TensorFlow Lite for Microcontrollers 

   - Introduction to TensorFlow Lite framework 

   - Adapting models for microcontroller deployment 

3. Model Quantization and Quantisation-Aware Training 

   - Principles of quantization in neural networks 

   - Implementing quantization-aware training 

4. Weight and Activation Quantisation 

   - Techniques for reducing precision of weights and activations 

   - Impact on model performance and accuracy 

5. Linear, Binary, and Ternary Quantisation 

   - Exploring different quantisation schemes 

   - Trade-offs between model size and accuracy 

  6. Model Pruning and Fine-Tuning Pruned Networks 

   - Strategies for removing unnecessary connections in neural networks 

   - Techniques for fine-tuning pruned models to maintain performance 

Laboratory Session: 

- Hands-on experience with model compression using quantisation and pruning techniques 

Day 3: ML Model Deployment and Applications 

The final day covers the practical aspects of deploying ML models on embedded systems: 

1. End-to-End ML Deployment Workflow 

   - Steps involved in taking a model from development to deployment 

   - Best practices for embedded ML pipelines  

2. Model Debugging and Testing 

   - Techniques for identifying and resolving issues in embedded ML models 

   - Ensuring model reliability and performance on target devices  

3. Case Studies 

   - Magic Wand: Gesture recognition using accelerometer data 

   - Wake Word Detection: Audio processing for keyword spotting 

   - Visual Wake Detection: Computer vision for device activation 

  Laboratory Session: 

- Deployment of computer vision models for image classification and object detection on microcontroller platforms 

 Throughout the course, participants will use a variety of software tools: 

- Python in Google Colab for model development and initial testing 

- MicroPython for programming microcontrollers 

- TensorFlow Lite Micro for deploying optimised models on embedded devices  

Hardware Used: 

- Arduino Tiny Machine Learning Kit with Arduino Nano BLE Sense and onboard sensors 

- Arduino Nicla vision, voice, and sense boards 

These hardware platforms provide participants with hands-on experience working with real embedded systems capable of running machine learning models. 

Skills Acquired 

Upon completion of the Embedded Machine Learning course, participants will have gained a robust set of skills highly valued in the IoT and embedded systems industries: 

1. Machine Learning for Embedded Systems 

   - Ability to design and implement ML models suitable for resource-constrained devices 

   - Understanding of the trade-offs between model complexity and device capabilities 

2. Model Optimisation Techniques 

   - Proficiency in applying quantization and pruning techniques to reduce model size 

   - Skills in fine-tuning optimised models to maintain performance 

3. TensorFlow Lite Implementation 

   - Expertise in using TensorFlow Lite for microcontroller deployments 

   - Ability to adapt existing models for embedded platforms 

4. Computer Vision on Embedded Devices 

   - Skills in implementing image classification and object detection on microcontrollers 

   - Understanding of efficient computer vision algorithms for low-power devices  

5. Embedded Systems Programming 

   - Proficiency in programming microcontrollers using MicroPython and C 

   - Experience with Arduino-based platforms and sensor integration 

 6. End-to-End ML Deployment 

   - Ability to take an ML model from development to deployment on embedded hardware 

   - Skills in debugging and optimizing ML models for specific embedded applications 

7. IoT and Edge Computing Fundamentals 

   - Understanding of IoT architectures and the role of edge computing 

   - Knowledge of how embedded ML fits into broader IoT ecosystems 

The Embedded Machine Learning course offers numerous benefits to participants: 

1. Cutting-Edge Knowledge 

   - Gain insights into one of the fastest-growing fields in technology 

   - Learn from experts at a world-renowned institution 

2. Practical Skills 

   - Hands-on experience with real embedded ML projects 

   - Ability to immediately apply learned concepts to real-world problems  

3. Career Advancement 

   - Acquire skills highly sought after in the IoT and AI industries 

   - Potential for career growth in embedded systems, AI, and IoT sectors  

4. Networking Opportunities 

   - Interact with like-minded professionals and academics 

   - Build connections within the embedded ML community 

5. Innovation Potential 

   - Develop the ability to create novel IoT and edge computing solutions 

   - Contribute to the advancement of AI in resource-constrained environments 

6. Academic and Research Prospects 

   - Solid foundation for further academic study in embedded ML 

   - Potential for research collaborations and publications  

Who Should Attend 

 The course is particularly beneficial for: 

1. Students aiming for a career in embedded systems, IoT, or AI 

2. Lecturers and professors planning to incorporate embedded ML into their curricula 

3. Industry professionals looking to upgrade their skills in AI and embedded systems 

4. R&D candidates pursuing careers in cutting-edge technology development 

 Future Prospects 

 The field of embedded machine learning is rapidly evolving, with new applications emerging across various industries: 

1. IoT and Smart Devices 

   - Development of intelligent, low-power sensors and actuators 

   - Creation of smart home and industrial automation systems 

2. Wearable Technology 

   - Design of health monitoring devices with on-device AI capabilities 

   - Development of augmented reality systems with embedded ML  

3. Autonomous Systems 

   - Implementation of ML models for drones and robots 

   - Creation of intelligent control systems for vehicles and machinery  

4. Edge Computing 

   - Development of distributed AI systems for real-time data processing 

   - Implementation of privacy-preserving ML techniques on edge devices  

5. Environmental Monitoring 

   - Design of low-power, intelligent sensors for ecological research 

   - Creation of early warning systems for natural disasters  

6. Healthcare 

   - Development of portable diagnostic devices with embedded ML capabilities 

   - Creation of personalized health monitoring systems 

By gaining expertise in embedded machine learning, participants position themselves at the forefront of these exciting technological advancements.  

The Embedded Machine Learning course at the Indian Institute of Science offers a unique opportunity to gain expertise in a field that is reshaping the landscape of technology. By combining theoretical knowledge with practical, hands-on experience, the course equips participants with the skills needed to innovate in the rapidly evolving world of IoT and edge computing. 

As we move towards a future where intelligence is increasingly embedded in the devices around us, professionals with expertise in embedded ML will play a crucial role in shaping this technological revolution. Whether you're a student looking to start a career in this exciting field, an academic aiming to expand your research horizons, or an industry professional seeking to stay ahead of the curve, this course provides the knowledge and skills you need to succeed. 

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