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Deep learning in life science

General data

Course ID:	1000-2M23DLS
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Deep learning in life science
Name in Polish:	Deep learning in life science
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Elective courses for Computer Science and Machine Learning Specific programme courses of 2nd stage Bioinformatics
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. view allocation of credits
Language:	English
Type of course:	elective monographs

Classes in period "Summer semester 2024/25" (past)

Time span:	2025-02-17 - 2025-06-08	Selected timetable range: weekly course term Go to timetable MO WYK TU W TH LAB LAB FR LAB
Type of class:	Lab, 30 hours more information Lecture, 30 hours more information
Coordinators:	Bartosz Wilczyński
Group instructors:	Ada Hryniewicka, Joanna Sułkowska, Bartosz Wilczyński
Students list:	(inaccessible to you)
Credit:	Examination

Course descriptions are protected by copyright.
Copyright by University of Warsaw.