INFORMATION FOR STUDENTS OF MACHINE LEARNING

You need to indicate your preferred choice of diploma seminars when recruiting for a second-cycle degree programme in the system of Internet Recruitment of Candidates (IRK). Assignment to the seminar is based on ranking lists and is announced at the end of September.

Registration for classes

You will be registered for the obligatory courses of the winter semester of your first year of study (unless you have previously passed their equivalents) by the Student office. You can choose your course class/lab group during the RDG round (direct registration for groups) or the groups exchange system. The deadlines for the respective registration rounds can be found in our faculty's USOSweb.

From the second semester of study onwards, you will need to register on your own for all courses (except the master seminar). The general section of this student guide contains detailed registration information and course group descriptions.

There are only Bootcamp, Ideas and informatics classes and seminar meetings in the first two weeks of October (although seminars and Ideas and informatics continue throughout the semester), which requires some attention to the planning of the course schedule and the early weeks of classes. Also note where each class is held - this will not always be 2 Banacha Street.

Choice of courses

The course schedule for second-cycle studies in Machine Learning requires completing elective courses for 24 ECTS (which corresponds to 4 standard courses of 6 ects each). You can choose them from the Elective courses for Machine Learning group or Elective courses for Computer science group (also form Concurrent and distributed programming group).

However, do not choose Statistical Data Analysis (1000-714SAD) - its syllabus is too close to Statistical machine learning (1000-317bSML), an obligatory course for ML.

In addition, with prior agreement with the vice-dean for student affairs, you can complete a course from

• Obligatory courses for 1st year 2nd cycle Computer Science
• Elective courses for 2nd cycle studies in Mathematics
• Elective monographic courses for 2nd cycle studies in Mathematics

as an elective course for ML. 

Note that according to the study programme, elective courses are intended to introduce students to advanced techniques with a focus on theoretical computer science or software engineering. This means that having the approval to recognise a course from the groups listed above as an elective course for ML is not automatic. We recommend to make sure about the possibility of recognising a course as an elective one by submitting an application in usos at the beginning of the semester.

If you have completed during your first-cycle studies:

• Deep neural networks with a Course ID: 1000-2M16GSN (ML equivalent: 1000-317bDNN)
• Robot Control with a Course ID: 1000-2M22RC (ML equivalent: 1000-317bRC)
• Ideas and informatics with a Course ID: 1000-217bIII (ML equivalent: 1000-317bIII)
• Statistical machine learning with a Course ID: 1000-2M21US (ML equivalent: 1000-317bSML)*
• Visual recognition: neural networks with a Course ID: 1000-2M18RO ( ML equivalent: 1000-318bVR)
• Reinforcement learning 1000-2M20UZW (ML equivalent: 1000-318bRL)
• Natural language processing with a Course ID: 1000-2M21NLP (ML equivalent: 1000-318bNLP)
• Explainable Machine Learning with a Course ID: 1000-1M18WUM (ML equivalent: 1000-319bEML)

uou will be exempted from passing the ML equivalent. If you have used the credit to settle a previous study term, you will be exempted from the ML equivalent but you will need to complete another elective course, but if the course has not been linked to a previous study term and not used to settle a previous study term, you can link it yourself to the ML programme.

In addition, in the academic year 2022/23 you may be exempted from passing the SML on the basis of a SAD credited with at least 4; ask your head of studies about the rules in the following academic years.

In your second year of studies you should also complete an internship/study visit. The internship supervisor is Dr Waldemar Pałuba (email: W.Paluba@mimuw.edu.pl) and the rules of the completing the internship (and templates of the necessary documents) can be found here, Polish version. Remember, the deadline for completing the internship/study visit is in the winter session.

Diploma examination process

In order to complete the first year of the master seminar, you need to have your master thesis topic approved. In order to complete the 2nd year of the seminar, you need to submit your thesis to the APD (you do not need to print it). You can only take the diploma examination once you have ended studies (i.e. you have completed all the courses required by your study plan). However, you may start arranging the examination date even before you have completed all your credits in Usos.

The master diploma examination is oral. The exam consists of a presentation of the master thesis (up to 15 minutes) and answers to 3 questions directly related to the master thesis topic.