Machine Learning (S2-PRK-ML)(in Polish: Machine Learning, stacjonarne, drugiego stopnia) | |
second cycle programme full-time, 2-year studies Language: English | Jump to: Opis ogólnyThe second degree program in Machine learning offered at the University of Warsaw was created as a response to the rapidly growing interest in information processing technologies in this area. The degree program is designed on the basis of well-functioning and long-established practices in the study field of computer science. The recently developed curriculum has been prepared taking into account current developments in computer science in the area of machine learning and artificial intelligence and their applications in the business.The content is designed to address the needs of both those students who view the knowledge and skills they will acquire during the studies as an asset in their career path, and those particularly gifted in exact science, who are planning a research career. The Faculty of Mathematics, Informatics and Mechanics is recognized and appreciated in the world. During the second cycle studies, the primary emphasis is on learning creative problem solving, the ability to build generalizations and pose questions. Graduates of the second-cycle studies become proficient not only in the use of selected information processing technologies in the field of machine learning, but they are also able to use the acquired knowledge and skills in applications unrelated to the studied discipline, for example in interdisciplinary research teams. As a result, graduates are prepared for careers that require significant knowledge of machine learning to cope with contemporary challenges facing computer solutions. At the same time, students are included in the research conducted at the university, which prepares the graduates to conduct scientific research activities and undertake doctoral studies. Studies in machine learning allow future graduates to acquire advanced knowledge and skills in techniques used in machine learning, including: statistical methods for machine learning, deep neural networks, reinforcement learning, and explanation of results obtained from machine learning procedures. They also become familiar with basic machine learning application domains such as visual recognition, autonomous device control, and natural language processing. As a result, graduates are prepared to design, oversee, and critically analyze IT projects with significant machine learning components, to serve in expert roles in machine learning, and to be leaders beyond the university world. Graduates of the Master of Science in Machine Learning have the knowledge and skills to pursue a third-cycle degree in computer science. Most classes are held in the building of the Faculty of Mathematics, Informatics and Mechanics, Ochota Campus, 2 Banacha St. Programming classes are held in modern computer laboratories. The field of study Machine Learning was created as part of the project Akademia Innowacyjnych Zastosowań Technologii Cyfrowych, in the scope of the Digital Poland programme, co-funded by the European Regional Development Fund. Enrolling in this field of study is associated with joining the project. |
ECTS Coordinators:
Qualification awarded:
Access to further studies:
Learning outcomes
The graduate has achieved the learning outcomes defined for the second-cycle degree programme in Machine Learning in Annex to Resolution No. 38 of the Senate of the University of Warsaw of 17 March 2021 on Machine Learning programme (UW Monitor 2021, No. 75).
On completing this curriculum the student:
• is ready to realize social obligations, inspire and organize activities for the benefit of social environment;
• has based in theory and well organized knowledge of fundamental techniques of machine learning and methodology of constructions and research in this field;
• knows high-performance data processing techniques used in machine learning;
• has based in theory and well organized knowledge of problems of robot control, in particular of motion kinematics, movement planning and orientation in space;
• has based in theory and well organized knowledge of problems of image classification and object detection;
• knows methodologies, topics, techniques and tools in natural language processing;
• has in-depth understanding of the branches of mathematics necessary to study machine learning (probability theory, statistics, multivariable calculus, and linear algebra);
• is able to implement own algorithms and use existing libraries with reinforcement learning procedures;
• is able to process big data sets;
• is able to apply methods developed to study structures used in machine learning as well as to use them in the analysis of domain data.