Content, progress and pedagogy of the module

The course gives a comprehensive introduction to machine learning and its application in manufacturing engineering. The field is concerned with learning from data and has roots in computer science, statistics and pattern recognition. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives

Knowledge

• Must have knowledge about the steps of a machine learning project pipeline.
• Must have knowledge about supervised and unsupervised learning methods.
• Must have knowledge about machine learning fundamentals, software, frameworks, tools, and public datasets.

Skills

• Must be able to apply the taught methods to solve concrete engineering problems.
• Must be able to evaluate and compare the methods within a specific application problem.
• Must be able to build a machine learning project pipeline.
• Must be able to understand the value of a machine learning solution in a business context.

Competences

• Must have competencies in analyzing a given problem and identifying appropriate machine learning methods to the problem.
• Must have competencies in understanding the strengths and weaknesses of the methods.
• Must have competencies in evaluating a machine learning solution’s advantages and limitations.

Type of instruction

The criteria of assessment are stated in the Examination Policies and Procedures, § 17.

Exam

Exams