MA Machine Learning

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Area of Studies

mathematics, informatics

Degree

Master

Degree (in English)

Master

Language(s)
  • English
Course Duration

2 years (4 semesters)

Department

Faculty of Mathematics, Informatics, and Mechanics

Tuition and Other Fees

Free of charge

Application Deadline(s)

from 09.06.2026 to 22.06.2026

Course Description

Course Profile

Studying Machine Learning, you will gain solid mathematical and computer science foundations to efficiently design, train and implement machine learning models.

We will provide you with knowledge on:

  • mathematical models behind machine learning,
  • classical supervised and unsupervised learning methods as well as deep learning techniques,
  • large-scale distributed computing systems needed to train machine learning models,
  • machine learning techniques used in visual recognition, natural language processing, robotics and reinforcement learning,
  • ways to explain how artificial intelligence models work.

Studying Machine Learning will give you skills in:

  • programming in Python using ML libraries, e.g. TensorFlow, PyTorch, scikit-learn,
  • implementing and training ML and AI models on real-world datasets,
  • creating ML processing pipelines and deploying ML models in production environments,
  • optimizing code and models for performance and efficiency

In addition, we will prepare you for the changing needs of the environment by developing your social competences in:

  • critical and analytical thinking - the ability to assess the quality of models and select appropriate algorithms,
  • solving problems using ML algorithms in real-world applications,
  • teamwork
  • communicating results - presenting analysis and recommendations in a way that can be understood by a wide o   audience,
  • the need for continuous improvement - ML is a fast-paced and dynamic field that requires constant learning of new technologies and methods.

Studying ML will provide you with not only theoretical knowledge, but also practical skills that are valued in the technology industry.

Where you can find a job after completing studies

With a degree in Machine Learning, you can find work in a wide range of industries, as machine learning is widely used in data analysis, automation and artificial intelligence. Common career paths include positions such as ML Engineer, Data Scientist, AI Researcher or ML Software Engineer. While tech companies such as Google, Meta, Microsoft and OpenAI are heavily recruiting ML specialists, the financial, medical, e-commerce or industrial sectors are also increasingly deploying AI models to optimize processes. You can find jobs in large corporations, start-ups, as well as in academic or research environments.

Are there different specialties and specializations at the field of studies

We do not offer any specialties or specializations in the Machine Learning field of study.

What will you learn during the studies

Mathematical foundations are a key part of the ML degree programme, as they pave the way to development of models and algorithms in the field of machine learning. We develop competence in fundamental mathematical skills, providing proficiency in advanced statistical methods and neural network architectures. Mathematical analysis supports the understanding of data and the interpretability of models, which is important for the ethical implementation of artificial intelligence.

During ML studies you will explore advanced neural networks, learn how to train, optimize and solve typical problems. You will get to know how to control intelligent systems combining decision-making algorithms and machine learning in robotics. You will master image analysis and natural language processing techniques. You will understand how reinforcement learning can be applied to robotics, games and recommender systems.

We also offer activities to develop practical skills in machine learning. You will learn how to communicate effectively in teams and manage projects. During internships you will gain experience in companies, working on real projects and networking with experts. You will learn how to design algorithms for large datasets and how to use modern computational tools. Team projects will help you improve your collaborative and problem-solving skills. And during the MSc seminar you will deepen your knowledge in your chosen area before defending your thesis.

Education Requirements

Information on qualification procedure is available at: irk.uw.edu.pl

Other Requirements

http://rekrutacja.uw.edu.pl/en/required-documents/

Apply

University of Warsaw

address:

Krakowskie Przedmieście 26/28 St.
00-927 Warsaw, Poland


phone: + 48 22 552 40 43, -48, -75, -02, +48 22 552 41 26
email: admission@uw.edu.pl
www: https://www.rekrutacja.uw.edu.pl/en