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Internship or study visit

General data

Course ID:	1000-319bINT
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Internship or study visit
Name in Polish:	Internship or study visit
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Obligatory courses for 2nd year Machine Learning
ECTS credit allocation (and other scores):	6.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	English
Type of course:	obligatory courses
Short description:	Obligatory vocational internship for students of machine learning programme.
Full description:	First-year students of machine learning are required to complete internships during their summer break (July, August). The internships last one month, with a total of 160 hours. If a public holiday falls on a work day during the internship, the duration of the internship should be extended to compensate for that day. Internships take place in companies dealing with machine learning applications. Alternatively, in place of the internship, the student may complete two or three several-day study visits in research groups working in fields related to machine learning.
Bibliography:	None
Learning outcomes:	The student is ready to * critically evaluate acquired knowledge and information; * recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution; * think and act in an entrepreneurial way.
Assessment methods and assessment criteria:	According to the rules of completing the internship during the second-cycle studies in Machine Learning https://dokumenty.uw.edu.pl/dziennik/DRD/Lists/Dziennik/Attachments/1081/DRD.2021.297.URD.16en.pdf

Classes in period "Academic year 2023/24" (in progress)

Time span:	2023-10-01 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable
Type of class:	Placement, 30 hours more information
Coordinators:	Waldemar Pałuba
Group instructors:	Waldemar Pałuba
Students list:	(inaccessible to you)
Examination:	Course - Grading Placement - Grading

Classes in period "Academic year 2024/25" (future)

Time span:	2024-10-01 - 2025-06-08	Selected timetable range: weekly course term Navigate to timetable
Type of class:	Placement, 30 hours more information
Coordinators:	Waldemar Pałuba
Group instructors:	Waldemar Pałuba
Students list:	(inaccessible to you)
Examination:	Course - Grading Placement - Grading

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