Machine Learning for Engineers

Machine learning (ML) is a branch of AI that focuses on learning from data to design automated systems that can improve their performance with experience. In recent years, machine learning has been used in a wide range of engineering applications, including: autonomous cars, predicting mechanical failure, quality assessment, automation of engineering tasks, robotic vision and intelligent control among others.

This course provides a thorough introduction to machine learning and hands-on experience with its practical applications. The topics taught in this course will cover fundamental principles in machine learning as well as the theoretical bases for its algorithms and how they can be optimally applied.

The focus is made on adaptation, combination and application of existing ML tools for solving engineering problems considering product development lifecycle, including product development, manufacturing, use and potential recycle/reuse/repurpose. At the end one should be able i) to identify suitable ML-based approaches for solving an engineering problem, ii) find right components, tools to implement the approach and iii) integrate and fine-tune those components to develop the solution.

Having successfully completed this course student will be able to acquire the following:

Knowledge:

• Learn the fundamental principles of supervised, unsupervised and reinforcement learning.
• Acquiring knowledge of using and finetuning ML to solve practical problems relevant for engineers.

Skills:

• Apply data handling, feature engineering and data pre-processing techniques
• Gain experience to systematically work with data to learn new patterns.

General competence:

• At the end of this course students will understand the strengths and limitations of well-known machine learning methods, and learn how to analyse data to identify trends.

Learning activities in this course include: lectures, preparing seminars, working on mini projects, and contribution to discussion.

The examination papers will be given in English only. It is also expected that English is used for answering the exam.