Curricula

Objective

In recent years, technologies based on data-driven AI algorithms have also become more common in industrial applications. In this course, you will learn the basics of how this technology works. In particular, you will learn how to analyse data and build machine learning algorithms in a Python (or equivalent) programming environment. In addition to numerical data, image and text data will be used as data. The skills learned will be applied to topics such as: predicting the quality of a production process, predictive maintenance and product quality control using machine vision,

Knowledge and understanding of applicable analytical, design and research/development techniques and methodologies and their limitations in the field of AI.

Ability to follow developments in AI and machine learning.

Ability to apply understanding of AI requirements to design and development.

Content

Data analysis: mean, standard deviation, distribution, probability.
Analysis of variance and regression.
Basics of Python programming language (or equivalent).
Common machine learning algorithms: linear regression model, decision trees and random forests, neural networks.
Image classification.
Processing textual data with a large language model (e.g. ChatGPT).

Qualifications

No prior knowledge required.

Assessment criteria, satisfactory (1)

Intermediate 1: You know the most commonly used techniques in machine learning. You can apply the most common machine learning techniques. You can also evaluate your implementation in a concise manner.

Satisfactory 2: You know the most commonly used machine learning techniques for different problems. You can select machine learning techniques and apply your technical knowledge in practice. In addition, you can make a cursory assessment of your implementation.

Assessment criteria, good (3)

Good 3: You are aware of the benefits of machine learning in an era of digitalisation. You know the most commonly used techniques of machine learning for different problems. You can apply your technical knowledge to robotics.

Excellent 4: You recognise the benefits of machine learning in the era of digitalisation. You know the most commonly used techniques of machine learning and can justify the use of these techniques for different problems. You can apply your technical knowledge in robotics.

Assessment criteria, excellent (5)

Excellent 5: You recognise the benefits of machine learning in an era of digitalisation. You have a good command of machine learning techniques and can apply them to robotics. You can apply your technical knowledge in practice and critically evaluate your implementation and justify its development.