Course Syllabus

Bialystok University of Technology Faculty of Computer Science
Field of study Data Science Degree level and
programme type
Engineer's degree
full-time programme
Specialization/
diploma path
Study profile academic
Course name Deep Learning E Course code DS1S5UGL
Course type obligatory
Forms and number of hours of tuition L C LC P SW FW S Semester 5
30 30 No. of ECTS credits 5
Program valid from 2025/2026
Entry requirements no English name yet ! (DS1S4UM2),  
Course objectives
Framework content
Other information about the course the course is related to the scientific research conducted at the University
Calculation: Student workload (in hours): Total
hours
Including
contact hours
Including
practical hours
30 30
30 30 30
4 4
10
51 51
Cumulative hours: 125 64 81
Cumulative number of ECTS credits: 5 2.6 3.2
Expected program-specific learning outcomes Knowledge Skills Social
competences
DS1_W03 DS1_U06
DS1_W07 DS1_U08
DS1_W09 DS1_U09
DS1_W16 DS1_U17
DS1_U19
Objectives and framework content formulated by dr hab. inż. Małgorzata Krętowska Date: 29/05/2025
Implemented in the academic year 2027/2028
 
Course objectives
1.
. -
Teaching methods
(stationary)
-
-
Teaching methods
(remote)
-
-
Assessment methods
-
-
Assessment conditions
-
-
Symbol of
learning outcome
Intended learning outcomes Type of tuition during which the outcome is assessed
Knowledge Skills Social
competences
Knowledge: the student knows and understands
E1
E2
Skills: the student can
E3
E4
Symbol of
learning outcome
Method of learning outcome verification Form of classes where verification takes place
E1 -
E2 -
E3 -
E4 -
Basic references
1.
. -
Supplementary references
1.
. -
Course coordinator: dr hab. Marek J. Drużdżel, dr hab. inż. Jacek Grekow, dr hab. inż. Małgorzata Krętowska, dr inż. Urszula Kużelewska, dr inż. Tomasz Łukaszuk Date: 03/03/2025