Machine Learning for Internet of Things (IoT)
Maskininlärning för sakernas internet (IoT)

EITP40F, 7.5 credits

Valid from: Autumn 2022
Decided by: Maria Sandsten
Date of establishment: 2022-10-10

General Information

Division: Electrical and Information Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: EITP40
Teaching language: English

Aim

The purpose of the course is to provide an introduction to artificial intelligence and machine learning techniques for IoT systems e.g. wearable sensors for health monitoring.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• For a passing grade the student must
• understand the IoT domain and the corresponding challenges and opportunities
• understand the state-of-the-art machine learning and artificial intelligence techniques
• understand the fundamental ideas behind the state-of-the-art machine learning techniques in the context of IoT systems e.g. in wearable sensors for health monitoring and medical informatics.

Competences and Skills

For a passing grade the doctoral student must

• For a passing grade the student must
• analyze the suitability of a given machine learning technique for IoT systems
• apply and implement the state-of-the-art techniques in machine learning and artificial intelligence in the context of IoT systems
• evaluate and validate the existing machine learning techniques for IoT systems, in terms of relevant domain metrics.

Judgement and Approach

For a passing grade the doctoral student must

• For a passing grade the student must
• show knowledge of the possibilities and limitations of artificial intelligence and machine learning in the context of IoT systems
• independently develop, train, and implement machine learning techniques on IoT systems and investigate the results obtained.

Course Contents

Introduction to IoT systems and the challenges and opportunities in this domain Introduction and foundation of machine learning and deep neural networks in the context of IoT systems e.g. for wearable devices and sensors for health monitoring and medical informatics; Machine learning for IoT systems and distributed resource-constrained platforms.

Course Literature

• Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. 2016.
• Lindholm, A., Wahlström, N., Lindsten, F. & Schön, Thomas B.: Machine Learning, A First Course for Engineers and Scientists.
• Pete Warden, D.: TinyML:, Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers.

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises, project

Examination Details

Examination format: Oral exam
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Programming, Basic probability, statistics, and algebra.

Further Information

Course Coordinator: Amir Aminifar amir.aminifar@eit.lth.se

Course Occasion Information

Contact and Other Information

Course coordinators: