Chalmers University of Technology: Chalmers University of Technology, situated in Gothenburg, Sweden, is a prominent technical university known for its world-class education and research in engineering, technology, and natural sciences. Chalmers fosters an entrepreneurial and collaborative environment and encourages innovation and sustainability. The university has strong ties with industry and offers students valuable opportunities for practical training and

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Data science and AI master's programme at Chalmers With the digital revolution, Data science and Artificial Intelligence (AI) has become an important part of our lives and society as a whole. In addition, the quickly emerging technologies for processing large-scale data and machine learning is creating a wealth of opportunities where automated decision-making is becoming a reality. Skilled data scientists and AI engineers are in high demand everywhere. With a solid foundation in machine learning, you will have a wide range of career opportunities. Data science is a highly cross-disciplinary field concerned with how to extract useful knowledge from data, for deeper understanding and decision support. It is based on a blend of methods in statistics and machine learning, together with computational techniques and algorithms for handling large-scale data. Examples of application areas include biology and other sciences, healthcare, business, finance, and different kinds of internet data. Computational methods range from algorithms for collecting and handling large-scale data, statistical methods such as Bayesian modelling, to machine learning techniques such as deep neural networks. AI is about building intelligent systems and is currently a rapidly evolving field thanks to recent advances in machine learning with large-scale data, where machine learning enables the computer to perform complex tasks without being explicitly programmed. Successful examples of this approach include machine translation, computer vision, game playing and self-driving vehicles. Through their use of large-scale data and machine learning, the fields of Data science and AI are closely connected. They are also connected in their application since it is common to first collect and analyse data to better understand the problem, and then to build algorithms and systems for decision support and autonomous decision-making. Therefore, with an increased demand for advanced information systems and computer applications in a wide range of areas, Data science, and AI are becoming necessary ingredients in software development in general. The overall aim of the programme is to educate engineers who can undertake a wide variety of challenges in handling and analysing different kinds of data, and who are able to use and develop software in complex data-intensive and AI-related applications. This requires a good understanding of both theory and practice, including the possibilities and limitations of existing and evolving technologies, and how to responsibly apply these in various situations. The courses of the programme will provide a solid foundation in machine learning, statistics, and optimization, with an depth understanding of the mathematical modelling techniques used for extracting information from large sets of complex data, and with the computational skills and algorithms for working with such data. You will also gain familiarity with a range of common problems within Data science and AI which can be solved with such techniques. Through a combination of theory and practice in the courses of the programme, you will gain an understanding of how and why certain models and algorithms work and will be able to identify their possibilities and limitations. You will be able to approach a real-world problem in a specific problem domain, combining existing and new methods to create an efficient solution. You will be able to continuously learn in these rapidly evolving fields, communicate with experts and non-experts in specific problem domains, and apply these technologies responsibly. You will also gain the insights to be able to understand and influence the role of Data science and AI in society. Show less

The master's programme runs for a duration of two years, leading to a Master of Science (MSc) degree. During each year, students can earn 60 credits (ECTS) and complete the programme by accumulating a total of 120 credits. Credits are earned by completing courses where each course is usually 7.5 credits. The programme consists of compulsory courses, compulsory elective courses and elective courses.

Compulsory courses year 1
During the first year the programme starts with four compulsory courses of 7.5 hp each that form a common foundation in Data science and AI:

Introduction to data science and artificial intelligence
Nonlinear optimization
Stochastic processes and bayesian statistics
Design of AI systems
These will give you an introduction and a good foundation for the field. The purely mathematical courses in statistics and optimization are important for data science and AI in several ways and form the mathematical foundations of machine learning. The applied courses will give you a good combination of applied theory and hands-on experiences. The courses will also include considerations of ethical, social, and environmental issues.

Compulsory courses year 2
In the second year, you must complete a master's thesis worth 30 credits in order to graduate.

Master’s thesis
Compulsory elective courses
You need to select at least two courses from the general group 1 and at least two from the profile specific group 2. You can then specialize on one of the profile tracks.

Group 1:

Algorithms
Options and mathematics
Applied Machine Learning*
Algorithms for machine learning and inference*
Computational techniques for large-scale data
Statistical learning for big data
Advanced databases
Group 2:

Causality and causal inference
Image processing**
High-performance computing
Machine learning for natural language processing
Basic stochastic processes and financial applications
Advanced probabilistic machine learning
Large scale optimization
Image analysis**
Advanced topics in machine learning
Spatial statistics and image analysis
Financial time series
*Only one of the courses between Applied machine learning and Algorithms for machine learning and inference can simultaneously be counted in the exam.
** Only one of the courses between Image processing and Image analysis can simultaneously be counted in the exam.

Elective courses
You will also be able to select courses outside of your programme plan. These are called elective courses. You can choose from a wide range of elective courses, including the following:

Algorithms, advanced course
Linear statistical models
Computational methods in bioinformatics
Applied signal processing
Distributed systems
Empirical software engineering
Linear integer optimization with applications
Bayesian statistics
Health Informatics
Deep machine learning
Artificial neural networks
Strategic management of technological innovation
Creating technology-based ventures

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You will need to book the accommodation after you have been accepted.

You can choose to live on campus or off campus in private accommodation.

How to book:

• Make a booking online after you have been accepted (in this case please let us know your choice when you apply).
• Register when you arrive - its not possible to reserve a room before arriving. You can arrive a few days before and book it

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