Artificial Intelligence
Artificial Intelligence (AI) is quickly growing to be integrated into our society. This integration is reshaping engineering, science, and design processes, requiring future engineers to adopt new skills, have adaptive mindsets, and embrace new responsibilities. Reports from organizations, such as Gartner and the World Economic Forum, point out the increasing demand for roles driven by AI adoption. This highlights the urgent need to develop critical AI skills for future engineers, emphasizing skill transformation, resilience, technology literacy, and AI-empowered information processing.
The AI specialization offers courses that help student develop essential and transferable skills, gain knowledge on AI-related topics, including both about, with, and in AI education, in line with TU Delft educational policy and as an integral part of their curriculum.
This package provides a wide range of choices and guide students to choose the courses that best fit their interests, prior knowledge, and professional ambitions. To help you compose a coherent elective set, the TPM-AI package groups courses into three complementary clusters and sets out a few simple guidelines.
For whom?
Master students at TPM and TU Delft.
What will you learn?
Core technical skills, such as programming, mathematics and statistics, algorithms, AI and machine learning skills, cloud, ethical and societal implications related to responsible AI and ethical practices, and soft skills and domain knowledge are essential AI skills that the following courses offer. You have the opportunity to select from the following courses depending on your interests, and skill area you want to develop further.
Rules of the game
To complete the AI Elective Package, you need to collect 15 ECTS from the following course list. You will need at least 5 ECTS from courses that are listed as compulsory for your MSc program at TPM. If a course is already included as mandatory in the core curriculum for your MSc program, it is not allowed to consider it as an AI elective course.
Course offering for TPM students per MSc package
Mandatory course (at least one of)
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| COSEM | EPA | MOT | Quarter |
1 | SEN163B | Responsible Data Analytics | E (N) | M | M | Q3 |
2 | TPM034a | Machine learning for sociotechnical systems | M | M | M | Q2 |
3 | EPA122A | Spatial Data Science | M | - | M | Q2 |
Elective courses for AI specialization
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4 | TPM039A | Statistical Methods for Causal Inference and Prediction | E | E | E | Q1 |
5 | TPM035B | Game theory and strategic decision making | E | E | E | Q2 |
6 | SEN182a | Human-centered care interventions | E (N) | E | E | Q2 |
7 | SEN1721 | Travel Behavior Research | E (N) | E | E | Q3 |
8 | SEN1622 | I&C Service Design | E (N) | E | E | Q2 |
9 | EPA112A | Programming for Data Science | E | - | - | Q1 |
10 | SEN1211 | Agent-based Modelling | - | E | E | Q2 |
11 | TPM030a | Introduction to Cloud as Infrastructure: The effects of the new business of computing on practice | E | E | E | Q4 |
12 | TPM014B | Ethics of AI | E | E | E | Q2 |
13 | TPM016A | Robots and Society | E | E | E | Q3 |
| Legend | |||||
| M = Mandatory | |||||
| E = Elective | |||||
| - = not an option | |||||
| E(N) = Elective only applicable for COSEM students who are not taking the course as part of their specialization track. | |||||
Course offering for TU Delft students
Students in any TU Delft master’s program are welcome to enroll in TPM’s AI-specialization courses. You may select freely from both the mandatory and elective lists—none of the courses are compulsory for you. Simply make sure the credits fit within the elective space of your own degree and that you meet any stated prerequisites. Upon completion of three courses, you can request a certificate of completion issued by coordinators.
We especially encourage you to explore courses that examine AI through a socio-technical lens—a hallmark of TPM education. Leveraging TPM’s unique expertise at the intersection of technology, policy, and society will give your AI skill-set a distinctive, future-proof edge.
Courses such as Socio-technical Perspective on AI Governance & Institutional Design, ethics of AI, and Robots and Society explicitly connect technical AI methods with ethics, governance, and complex-system dynamics. By combining these with domain-oriented electives (e.g. Travel Behavior Research) you will gain a uniquely holistic view of how artificial intelligence interacts with people, organizations, and society at large.
Course overview
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This course teaches students how to work with data in meaningful ways. Over eight sessions, students learn how to collect, clean, explore, and analyze data, while thinking critically about privacy, bias, and fairness. They will write code, build machine learning models, and develop visual and written data stories. Weekly labs and a group project help students apply what they learn to real-world challenges.
Relevance to AI or Essential Skills for AI
This course gives students a strong foundation in data skills that are essential for working with AI. Students will learn how to create machine learning models and understand how to evaluate them for fairness and transparency. These skills are important for building responsible and trustworthy AI systems.Background Requirements for Elective Students (TPM MSc and TU Delft Students)
Python programming knowledge is required.Key Motivations for Taking the Course
This course is for students who want to learn how to use data and AI to address real social and policy challenges. It combines hands-on coding with critical thinking about the impact of technology. By the end, students will be able to build machine learning models and apply them responsibly in team-based projects that reflect real societal concerns. -
Aim
This course aims to equip students in the socio-technical domain with a deep understanding of machine learning.Relevance to AI or essential skills to AI
This course aims to prepare students for the challenges and questions they may encounter in their future careers in the socio-technical domain, involving Machine Learning.Background requirements for elective students
Students require prior knowledge of linear algebra, statistics, probability theory and data structures. Furthermore, basic Python programming knowledge is required.Key motivations for taking the course
Machine Learning (ML) is increasingly seen as a crucial part of the puzzle to solving the socio-technical challenges of today's networked and urbanised knowledge-driven societies. Successful adoption of ML does, however, not only require skilled computer scientists who do hard-core programming. Also, professionals are needed who have both the domain knowledge of socio-technical systems and a profound understanding of ML. That is the niche of this course. -
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This course equips students with the theoretical foundations and practical tools needed to uncover cause-and-effect relationships in complex socio-technical systems. The modern causal inference toolkit is widely used not only in science but also in business and public policy. Uber and other companies routinely apply causal methods to enhance customer satisfaction and achieve their strategic goals. During the COVID crisis, causal epidemiological studies based on observational data guided critical policymaking. Governments and international organizations, as part of the evidence-based policy making agenda, increasingly call for impact evaluations to assess whether policies exert a causal effect on outcomes of interest. The course teaches the statistical theory upon which causal inference and AI methods are based. Students acquire coding skills along the way as a by-product of working through the examples and doing a scientific replication. Prerequisite: Introductory-level knowledge of probability and statistics.
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This course teaches students how to analyze and solve real-world challenges through a game-theoretic lens and, in general, through strategic decision making. To do so, students will learn basics of game-theoretic methods, but also will work on a group project based on an article in the domain they like, where they will practice application of these methods on the problem they find relevant.
Relevance to AI or Essential Skills for AI
This course gives students a strong foundation in game-theoretic skills that are complementary to their AI skills. Game-theoretic and AI methods can be often combined to solve real world-problems more effectively. Understanding the mathematical assumptions of both will help the students to make the right choice regarding the most suitable methods when solving real-world problems.Background Requirements for Elective Students (TPM MSc and TU Delft Students)
Basic mathematical skills (i.e. those students learned in calculus and linear algebra courses). Programming skills are an asset.Key Motivations for Taking the Course
This course is for students who want to learn how to address real-world challenges more strategically, and when specificic methods or their combinations, such as those based on game theory, are suitable to solve such challenges. -
Health and care are among the most impactful and sensitive application domains for AI. In this course you will learn to analyze the weaknesses of today’s “standard-of-care” from an individual patient, carer, and clinician perspective. You then explore how AI-empowered digital technologies (e.g., decision-support, recommender systems, information retrieval, ML, generative and explainable AI, sensor-based platforms, and other forms of technology) can tackle those shortcomings and design or prototype a human-centered, value-driven, and safe innovation in close collaboration with domain experts. By the end of the quarter, you and your team will have produced a proof-of-concept solution—grounded in Human-centered-AI principles—and an evaluation plan showing how it improves real-world outcomes.
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This course focuses on modelling, explaining and understanding travel behavior. The course introduces the various modelling paradigms that exist, namely the econometric, psychological, geographical, biographical, marketing and sociological paradigm. Each paradigm has its own reasons to study travel behavior, and associated theories and statistical methods. Three statistical methods that figure prominently in the modelling paradigms are introduced in this course: structural equation models, latent class choice models and latent class clusters models. The latent class (cluster and choice) models are unsupervised machine learning methods (clustering). Knowledge about basic statistics and data-analysis (probability density functions, hypothesis testing, regression) is required as prior knowledge.
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This course focuses on how to design practical ICT services (with AI, Internet-of-Things, Cloud Edge computing) and develop a valorization plan. The technology context includes AI, Internet-of-Things, mobile technologies, Cloud Edge computing, which have been enabling a range of new ICT services. Since these services are typically offered in ecosystems of interdependent actors, designing services that add value for users as well as ecosystem stakeholders is challenging.
The course contains two main parts. In the first part, it is about designing innovative services that meet the needs of users. You will design service mockups and interview users to test your ideas. In the second part, it is about bringing that service idea to reality. You will design a valorization plan that specifies how to deal with external technologies, stakeholders and revenues.
Our SEN1622 course is practical and hands-on: you will create, test and plan your service ideas. At the same time, you will learn how to support design choices using relevant kernel and design theories.
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This hands-on course provides a solid foundation in Python programming and data analysis techniques. Students will explore and practice the key stages of the data analysis pipeline, including data collection, cleaning, transformation, visualization, and modeling. Through hands-on lab assignments and projects, they will gain experience with essential libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn, and learn how to use these tools to build and evaluate machine learning models. The course begins with a programming refresher covering fundamental concepts like algorithms, data structures, control flow, and Object-Oriented Programming. Designed to be both practical and forward-looking, this course equips students with the technical skills and mindset needed to contribute to AI and machine learning projects, as well as MSc theses that involve programming, data science, and intelligent systems.
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This course focuses on the fundamentals of Agent-Based Modeling (ABM) and complex systems theory. Throughout the course, students work in small groups to develop their own agent-based model of a predefined system using the Mesa library in Python. In parallel, students explore the theory of complex systems and their key properties such as emergence, self-organization, and adaptation. In this course, students develop essential programming and modelling skills, which are crucial for AI development and the modeling of complex systems in the field of artificial intelligence.
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Why is there a rush to AI? What is the role of cloud computing in the multi-trillion dollar valuation of tech companies? Are mobile phones really personal devices? How has the way software is produced today changed the theory and practice of computing? And, what are the implications of the technical breakthroughs pushed by big tech companies (Google, Apple, Amazon, Microsoft and NVIDIA) for our societies? These are some of the questions we will ask during this course. The thesis of this course is that contemporary computational infrastructures present an environment for software production that has become a key driver of political and economic developments globally. With computational infrastructures (CI) we refer to cloud + mobile devices as their accessories, and by software production, we refer to the creation of economic value through the engineering of software-based services. In this course, we will study how CI, developed and controlled by a handful of corporations in the business of computing, is vying to become the default environment for a movement that will transform relationships of production across the globe
Relevance to AI: You will learn about the historical and current forces that brought the current “AI” cycle into being, as well as how tech companies aim to change software production and the making of economic value with "AI".
Motivations for taking the course: After taking the course, you will be able to better understand the political and economic impact of AI in the world and connect its impact on the experience of users or the make-up of everyday environments to these broader changes.
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This course studies broadly ethical, societal, and descriptive issues that arise in relation to artificial intelligence. The following kinds of questions are examined: How can AI systems be built to promote, protect, preserve, and foster the pursuit of various values related to human well-being? What risks and uncertainties need to be accounted for in such systems? To what extent is it possible to empower individuals with applied systems while limiting the scope to which they can harm each other and themselves? This course discusses AI-related technologies such as Machine Learning and Deep Neural Networks, but also Big Data, Computer Simulations, Social Media, and Robotics. The following epistemic values are examined, among others: explainability, predictive accuracy, internal coherence, external consistency, unifying power, theoretical fertility, simplicity. The following normative values are examined, among others: trust, honesty, justice, tolerance, respect, responsibility, freedom.
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This course aims to learn to argue about moral, social, and epistemic values that students involved with AI (computer scientists, engineers) will encounter in their professional practice. The course focuses on contemporary pressing topics such as:
- Introduction to theories of normative ethics: utilitarianism, deontology, virtue ethics. The distinction between descriptive and normative claim, epistemology and ethics, values, norms, and virtues.
- Ethical and epistemological issues in AI: identification of six different sources of ethical and epistemological concerns in AI.
- Bias in AI: Automated-decision making systems are polluted with different forms of bias that can be found at a different levels. Here we address the different forms of bias and what can be done about it.
- Trustworthy AI: What does it mean to trust an AI system? is there more than one way to trustworthy AI? can we design trust? In this unit, we will address questions such as these. We will also present and discuss the two major approaches to trustworthy AI: transparency and computational reliabilism. Lights and shadows of both.
- Explanatory AI and the right to explanation: The GDPR grants the so-called "right to an explanation." But what does this means, and how far have we got into explanatory AI? In this unit, we explore the scope and limits of current approaches to explainable AI and how this affects users' rights
- Privacy in AI and doxing: there are different ways of studying and understanding privacy. In addition, this unit discusses the value of privacy in AI in comparison with other values, such as explainability, accuracy, bias, and fairness.
Register for this elective
Please fill in your application in My Study Planning and enroll this elective package in Brightspace.
Contact details
In case of questions, please contact the coordinators Helma Torkamaan en Iulia Lefter.
