Engineering with AI

TI-Mi-225

Note: there are exams in week 2.6. These are not in the normal exam week of week 2.10. If you are already planning your Christmas holidays, please take this into account!

Ultimately, one of the TU Delft’s objectives is that all students at TU Delft should be exposed to AI (Artificial Intelligence) education. That is why the TU Delft has recently launched 24 Delft AI Labs. Each faculty has now several AI labs, that aim to combine AI research with topics of interest from the faculty. For example, the faculty of Aerospace Engineering is investigating the use of graph neural networks to predict air traffic delays in a network of interconnected airports.

Students that complete this minor programme will - in their MSc programme of their faculty - gain access to the 15 EC course “Interdisciplinary Advanced Artificial Intelligence Project” (see TU Delft study guide). Together, this minor and this MSc course will allow you to do a MSc thesis project that combines AI with topics of interest of your faculty.

Furthermore, it is highly likely you will encounter AI techniques in your job after your studies. In this minor you will learn how to use AI techniques such as neural networks. At the end of the minor programme you will work together with an expert researcher from your faculty or one of the Delft AI Labs. Together with them you will develop or investigate an AI solution as part of the “Capstone Applied AI” project.

In this minor program you will also learn how AI and machine learning algorithms work and how you can apply them the right way. You will also learn the pros and cons of AI techniques, their limitations and possible associated ethical problems. You will get to know all the ins and outs of AI; you will be able to tune settings or to use specific AI algorithms using toolboxes, and will learn what is ‘under the hood’ of the AI toolkit.

By the end of this minor you will:

know how to apply AI techniques in your specific study field,
how to reformulate problems of your field such that they can be solved by AI algorithms,
recognize the limits and failures of AI solutions and know how to try and fix them.

Faculty of Electrical Engineering, Mathematics and Computer Science
ECs:	30
Language:	English
Maximum participants:	150
Non-selection minor:
Selection minor:
Education period(s):	Q1+Q2

Register for this minor

Participating institutions

Faculties EEMCS

For whom?

This minor is available for bachelor’s students from the TU Delft, and some programmes of Leiden University, and Erasmus University of Rotterdam. Students from other universities are unfortunately not eligible to follow this minor. If you are a student from Leiden or Erasmus, please check the minor admission matrix to see whether you can apply for this minor (based on which faculty you study at).

All BSc students from TU Delft except Computer Science BSc students can enrol for this minor. If you do a double BSc degree that includes computer science (for example: math and computer science) you are also not allowed to take this minor.

Who cannot attend?

Computer Science BSc students
Double BSc degree students with Computer Science (such as math and CS),
HBO students,
Students that are not at TU Delft, Leiden University or Erasmus University Rotterdam
Students from Leiden and Erasmus that are not eligible according to the minor admission matrix.

Information for bachelor’s students from Applied Mathematics (BSc TW):

The following course replacements are required for the BSc Technische Wiskunde (TW) students.

Engineering with AI Minor (TI-Mi-225) course:	The content equivalent for BSc TW students:	To be replaced by:
TI3170TU Introduction to Python Programming for AI	AM1090 Introduction to Programming	CSE2220 Signal Processing

Prerequisites

Knowledge of university-level mathematics (calculus, linear algebra, probability, and statistics) is recommended. You are responsible to check that you have the required knowledge and acquire it (self study 15-20h). Especially for students from IDE / IO, TPM / TB, and BK / A+BE, Leiden and Erasmus, self-study in the summer may required, because not all linear algebra and statistics is covered in the BSc programme. You can find a detailed description of the self-study material here: https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=66128

What will you learn

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 and describe how algorithms operate on data.
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 their 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.

Course overview

Below is a short description of all courses. For more information, please see the studyguide. (Note: The Study guide currently contains information about the 2023-2024 minor programme and earlier editions. The description of the courses in the new minor programme of 2024-2025 will become available soon.)

Quarter 1

[TI3170TU] Introduction to Python Programming for AI (5 EC)

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.

[TI3145TU] Machine Learning and Introduction to AI (5 EC)

In this course we will give you a brief introduction and overview of the field called AI – artificial intelligence. In the rest of the course we focus on machine learning: how algorithms learn from data. You will learn the fundamental concepts of machine learning, and various machine learning algorithms, such as linear regression, k-nearest neighbors, support vector machines and neural networks. You will learn how to train and fine-tune machine learning algorithms on datasets using the scikit-learn toolbox in Python.

[TI3140TU] Introduction to Responsible AI Engineering (5 EC)

In this course, you will learn about the philosophical perspectives on AI and responsible AI engineering, as well as examine AI`s impact on society through a range of contemporary case studies. You will look into the cultural, institutional, environmental and political dimensions of AI engineering and will learn to reason critically about the use and development of AI-based technologies.

Quarter 2

[TI3160TU] Natural Language Processing (5 EC)

This course provides an introduction to natural language processing (NLP), covering both theoretical concepts and practical applications of NLP techniques. Through this course, you will gain a foundational understanding of the fundamental concepts related to NLP and become familiar with various NLP techniques. Additionally, you will have the opportunity to gain hands-on experience implementing NLP techniques using programming tools in Python. Furthermore, this course will expose you to some of the latest NLP models currently used in production, including BERT, transformers, and large language models. You will also explore real-world applications of NLP, such as chatbots, to gain a better understanding of the practical use of NLP techniques.

[TI3155TU] Deep Learning (5 EC)

In this course, we will look at the foundations of deep learning, that is modern, multi-layered neural networks that have shown remarkable success in various tasks with large datasets. You will learn about core concepts of different deep learning techniques such as Multilayer Perceptron, Convolutional Networks and Recurrent Networks, through practical applications in simple, real-world scenarios. You will work at the model implementation using PyTorch library in Python.

[TI3165TU] Capstone Applied AI project (5 EC)

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.

Education methods

See the studyguide for more information

Register for this minor

To register, please follow the procedure described at Minors - English page, or Minors- Dutch page.

Participating institutions

Faculty of Electrical Engineering, Mathematics, and Computer Science.

Frequently asked questions

Can I follow this minor if I am an MSc student?

No, the Engineering with AI minor is only for BSc students.

Can I follow this minor If I am not a student from TU Delft?

Yes, there are several programmes from Leiden and Erasmus that are eligible. Please check the minor admission matrix to see whether you can apply for this minor.

Can I replace the course on Python programming?

Some students have already done a course on Python in their bachelor’s programmes and wonder whether they can get a replacement for the Python course in this minor programme (TI3170TU). Our answer: we cannot offer a replacement for TI3170TU, unless you are a Mathematics student from the TU Delft (in which case you should follow the replacement table presented above).

I did a programming course (or other course) at faculty XYZ, can I replace a course in the minor programme by this course?

No.

I have followed the minor previous academic year, but I did not finish it. Do I need to enrol again?

You don't have to enrol again for the minor. You will automatically be registered for next years, and you can follow the courses of the minor. You however still need to register for the exam, Brightspace, etc. like when you are normally taking a course.

I took the AI minor in 2021-2022 or 2022-2023 but I did not finish it. What do I need to do to finish the minor?

The EC's you have obtained in previous years remain valid, even if the courses are not given in the current academic year anymore. Algorithms and Datastructures should be replaced by Natural Language Processing or Deep Learning. If you did started the minor in 2022-2023, you are also still allowed to take the ADS course in 2024-2025 of the CS minor, course code: TI3111TU. If you still have to complete the Capstone 10 EC variant, which does not exist anymore, you have to replace it by the Capstone 5 EC variant. The remaining 5 EC need to be filled by Natural Language Processing (5 EC) or Deep Learning (5 EC). You have to replace all your missing EC's, so that in the end you have 30 EC for the minor. You cannot use Natural Language Processing or Deep Learning as a replacement twice. Your enrolment for the minor will automatically remain valid, you only have to register for the exam on time and for the Brightspace page for the courses. If the course has mandatory projects or homeworks you also need to complete those. Please see the course Brightspace page or Studyguide.

I took the AI minor in 2023-2024 but I did not finish it. What do I need to do to finish the minor?

The EC's you have obtained in previous years remain valid, even if the courses are not given in the current academic year anymore. In the academic year 2024-2025 we have a new Python course, [TI3170TU] Introduction to Python Programming for AI (5 EC). For the academic year 2024-2025 you can decide whether you want to take the new or the old Python course, for the academic year 2025-2026 it will be mandatory to take the new Python course and you are not allowed to take the old Python course anymore. We recommend you to take the new Python course because it offers a better fit to the AI minor programme. You have to replace all your missing EC's, so that in the end you have 30 EC for the minor. Your enrolment for the minor will automatically remain valid, you only have to register for the exam on time and for the Brightspace page for the courses. If the course has mandatory projects or homeworks you also need to complete those. Please see the course Brightspace page or Studyguide.

When are the exams and resits?

The exams will take place in week 1.9 and / or week 1.10 during Q1. Note that during Q1 there is also a midterm exam (optional) for the Python course. During Q2 the exams will take place in week 2.6 just before the Christmas holiday. After the Christmas holiday, you will start to work full-time on the Capstone project during weeks 2.7-2.10. The Capstone project finishes in week 2.10 with a final presentation that you will have to attend. In week 2.9 or week 2.10 there will be the resits for the Q1 courses. Deep Learning and Natural Language Processing will have their resits in Q3. The Capstone project has no resit, if you fail the Capstone project you will need to retake it next year.

Do I have to be physically present during the minor?

The exams take place on the campus so your physical attendance there is mandatory. Furthermore, the Capstone is the mandatory group project. We recommend you to be present on the campus so you can effectively collaborate with your group members. For the Capstone, there will be mandatory moments for you to attend campus sessions, such as group presentations. The course Introduction to Responsible AI Engineering has a mandatory tutorials you need to attend during Q1. Please consult the Studyguide and Brightspace pages of the courses for more information.