Engineering with AI

It is highly probable that you will be influenced by AI during your studies and in your professional life. In this minor you will be educated to actively employ AI techniques in your field of studies and you will acquire the ability to understand AI and work together with AI expert engineers to reason about and build dedicated AI solutions.

If you have an interest in creating AI-enabled solutions themselves, this minor is set up for you. It will require your technical understanding of both the underlying data fed into the AI system and the algorithm running the AI system. You will also be acquainted with the limitations and ethical considerations of AI. You will get to know all the ins and outs of AI, will be able to tune settings or implement specific AI algorithms in software, and will learn what is ‘under the hood’ of the AI toolkit.

By completing this minor, you should be able to know how to apply AI techniques, define AI problems and solutions in your field of expertise, and will be able to recognize limits and failures of AI solutions applied to the field and mitigate them.

Note on the future of this minor

This minor starts as a pilot and with each new year of its operation we plan to revise (and improve) its programme and open it to a larger cohort of students (eventually also aiming at Erasmus University Rotterdam and Leiden University students).

Eligibility criteria

In terms of credit points a minimum of 80 EC obtained from your major programme is required. In terms of prior knowledge, basic knowledge of linear algebra and probability theory and basic programming skills are needed to attend this minor.

Students that will not be allowed to attend this minor are:

  • HBO students,
  • students outside of TU Delft, and
  • Computer Science undergraduate students of TU Delft.

As the pilot for the academic year 2021-2022, the minor is only open to TUD students.

Engineering with AI Minor (TI-Mi-225) course: The content equivalent for BSc TW students: To be replaced by:
TI3105TU Introduction to Python Programming AM1090 Introduction to Programming CSE2520 Big data Processing
TI3111TU Algorithms & Data Structures TI1520AM Algorithms & Data Structures CSE1400 Computer Organisation

Learning objectives

The main learning objectives are as follows, split per three core pillars:

  • Computational thinking:
    The student will be able to explain the basic concepts of computational thinking, describe how algorithms operate on data, and discuss the differences in the complexity of algorithms.
  • Artificial Intelligence:
    The student will be able to describe the fundamental concepts and techniques of AI, explain the possibilities and limitations of AI systems and the importance of their validation, and can apply at least one AI technique or analyse an AI application area, preferably in the field of the major.
  • Societal impact:
    The student will be able to examine the technological, societal and regulatory perspectives on AI, assess the impact of deploying AI-based solutions and interventions on individuals, organizations and society, and apply ethical considerations in the design of its own AI system in the field of his/her major.


Quarter 1

  • [TI3105TU] Introduction to Python Programming.
    The course aims at achieving a programming level that is needed to be able to implement AI algorithms. You will be able to work with external libraries, which can also be applied to the use of popular AI packages used by academia and industry.
  • [TI3140TU] Introduction to AI and Engineering Responsible AI.
    The course aims at explaining the most common views on what AI is. You will be able to examine the technological, societal and regulatory perspectives on AI and assess the impact of deploying AI-based solutions and interventions on individuals, organisations, and society.
  • [TI3111TU] Algorithms and Data Structures.
    he course aims at providing foundational knowledge of computer science concepts required in programming AI algorithms and manipulating AI data structures.

Quarter 2

  • [TI3145TU] Introduction to Machine Learning.
    The course aims at learning the basic concepts underlying machine learning techniques and applying and fine-tuning Python machine learning algorithms on various datasets.
  • [TI3150TU] Capstone Applied AI project.
    In this course, you will apply the topics learned in the previous minor courses on a group project with regards to your field of study. The project will be supervised by faculty staff and PhD students from the major field of study of your major.

Responsible education programme

Computer Science and Engineering

More information