Master onderwijs in AI, Data & Digitalisatie
De volgende onderwijsprogramma's en modules zijn bedoeld voor aankomende en huidige masterstudenten met interesse in Artificial Intelligentie en technologie, die op zoek zijn naar informatie om zich verder te bekwamen in AI. De voertaal van al deze onderwijsprogramma’s en onderdelen is Engels, dus verdere informatie hierover wordt in het Engels gegeven.
Full AI master Programmes
The Master programme Computer Science programme is built on the strengths and innovative power of our research groups. It will provide students with the flexibility to focus on ‘Data Science and Technology’, ‘Software Technology’ and/or ‘Artificial Intelligence Technology’, while also providing a broad basis that equips students to continue innovation in the future.
- The Data Science and Technology track of the Computer Science MSc programme will show you how to engineer and develop systems capable of processing and interpreting massive data sets to extract important information. Fundamental and practical issues of data analysis will be addressed, including e.g. the security of data and software, visualization of information, decision making from data, and high performance computing algorithms.
- The Artificial Intelligence Technology MSc Track will cover the algorithmic foundations of AI systems, addressing not only topics in machine learning and intelligent algorithms but also foundational topics in system and software engineering and data management. This track is designed for students with a keen interest in AI and who want to focus on the development and engineering of systems using AI to solve problems across a variety of application domains.
MSc Robotics is situated at the intersection of mechanical engineering and artificial intelligence. Knowledge of algorithms alone is insufficient: mechanical engineering knowledge is indispensable once movements and forces are involved, and ethics are crucial when estimating the impact on society. Our future society needs robotics engineers who understand how algorithms can be applied. They can ensure that mechanical systems learn and interact in complex environments, and they can also use their knowledge to develop robots for a healthy society. MSc Robotics offers students a multidisciplinary education, allowing them to develop innovative and intelligent products and systems that meet todays and tomorrow’s challenges.
AI Elective Courses
Master students in all subject-related domains (such as maritime engineering, policy analysis, industrial design) are often interested in the application of AI. They have several options for elective courses that fit into most TU Delft master programmes. By taking these courses, students can acquire the skills needed to incorporate such techniques into their thesis project.
- The selection of AI master courses presented below can be chosen as electives
- It is a non-exhaustive list of courses offered in Q1 of the TU Delft academic year
- These courses are generally open for all master programme students, and best suited for master students starting their second year.
- Students are encouraged to complete 15 EC in AI (related) courses if the elective space allows for this.
Master electives in AI, Data & Digitalisation in Q1 (2021-2022)
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In this project-based course you will apply basic artificial intelligence methods to a challenging practical problem in one of the focus domains of TU Delft. You will work in a multidisciplinary group of 3-4 students and will be supervised by an experienced researcher from one of the TU Delft faculties. The projects cover a variety of topics and can focus, for instance, on training, tuning and analysing machine learning models on existing data, on reproducing previous AI research and more.
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This course aims to give students from different technical backgrounds a better understanding of a range of machine learning techniques. The course focuses on understanding how to use these different techniques, rather than on trying to improve the techniques themselves. To achieve this, the course demonstrates how machine learning can be used in different domains and for different types of data. Teachers from different faculties will apply the techniques they teach to their own domain of research to give students experience with a wide range of the topics studied at TU Delft. Additionally, the course will address some of the ethical and societal issues around machine learning, such as privacy and bias.
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This course offers an introduction to scientific computing: programmatically modelling problems and analysing data. It uses Python and a number of tools and libraries often used for scientific computing. The course provides a mix of theory and skills, which are practiced using lab assignments (using Python), and discusses the following subjects:
- Programming: basic programming in Python
- Jupyter Notebooks
- Scientific computation with libraries such as Pandas, NumPy, SciPy
- Visualization with libraries such as Matplotlib
- Other programming tools and topics (when time permits).
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Many image processing and analysis techniques have been developed to aid the interpretation of a range of images (e.g. satellite remote sensing, but also other types of raster datasets like climate data), and to extract as much information as possible from the image data. Recent advances in remote sensing and computer science have, moreover, resulted in an explosive growth of image data sets and of data analysis techniques such as machine learning. This evolution is both a challenge and opportunity, as it requires specific techniques to explore, analyse and leverage the data. This course provides an overview of tools and techniques to explore, analyse, and visualise image data, and also on the implementation of big data analytics techniques on these data that can exploit growing data archives.
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Many image processing and analysis techniques have been developed to aid the interpretation of a range of images (e.g. satellite remote sensing, but also other types of raster datasets like climate data), and to extract as much information as possible from the image data. Recent advances in remote sensing and computer science have, moreover, resulted in an explosive growth of image data sets and of data analysis techniques such as machine learning. This evolution is both a challenge and opportunity, as it requires specific techniques to explore, analyse and leverage the data. This course provides an overview of tools and techniques to explore, analyse, and visualise image data, and also on the implementation of big data analytics techniques on these data that can exploit growing data archives.
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The core of social intelligence is our ability to understand and interpret the social signals of someone when communicating with them. Social intelligence has been argued to be indispensable, and perhaps the most important factor for success in life. Social Signal Processing (SSP) is the new and emerging domain aimed at understanding social interactions through machine analysis and production of nonverbal behaviour. In this course you will learn how next-generation computing can be equipped to make use of such social signals by giving it the ability to recognize and produce human social signals and social behaviours. Think about turn taking, politeness, disagreement, emotions, rapport. You will learn about relevant findings in social psychology, and computational techniques that allow systems to use social signals – becoming more effective by detecting and simulating (e.g. in virtual agents) blinks, smiles, crossed arms and laughter. Such socially aware computing can be used in robots, virtual agents, smart homes, crowd monitoring and much more.
Collection of Master electives in AI, Data & Digitalisation in Q4 (2021-2022)
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The course introduces the basic concepts of machine learning (supervised, unsupervised, reinforcement learning) and relates them to artificial intelligence and data analytics. Important machine learning algorithms are introduced and implemented in Python. On this basis, machine learning methods are applied to transport and multi-machine systems.