Deep Learning for Science
BASIC DATA
course listing
A - main register
course code
YMX8170
course title in Estonian
Masinõpe sügavate närvivõrkudega
course title in English
Deep Learning for Science
course volume CP
-
ECTS credits
6.00
to be declared
yes
assessment form
Examination
teaching semester
autumn
language of instruction
Estonian
English
Study programmes that contain the course
code of the study programme version
course compulsory
LAFM23/25
yes
YAFM02/22
yes
Structural units teaching the course
LT - Department of Cybernetics
Course description link
Timetable link
View the timetable
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Kursus annab teadmised süvaõppest (deep learning) ja selle rakendamisest teaduslike probleemide lahendamiseks.
course aims in English
The course gives knowledge about deep learning and its application in solving scientific problems.
learning outcomes in the course in Est.
Aine läbinud üliõpilane:
- analüüsib, millal ja millist süvaõppe meetodit rakendada;
- hindab süvaõppe meetodite kasutamise plusse ja miinuseid eri probleemide juures;
- on võimeline töötama tegelike andmestikega;
- valib ja kasutab teadusliku probleemi lahendamiseks sobilikke algoritme ja meetodeid;
- hindab süvaõppealgoritmide tulemuste sisukust ja kvaliteeti;
- seletab põhilisi matemaatilisi printsiipe ja tehnoloogilisi lahendusi süvaõppes;
- kasutab iseseisvalt süvaõppe meetodeid teadusliku probleemi lahendamiseks.
learning outcomes in the course in Eng.
After completing this course, the student:
- analyses in which situations to use particular methods of deep learning;
- estimates advantages and disadvantages of methods of deep learning in solution of different problems;
- is able to work with real data;
- chooses and uses suitable algorithms and methods to solve scientific problems;
- estimates content and quality of results of algorithms of deep learning;
- explains main mathematical principles and technological solutions of deep learning;
- independently uses methods of deep learning in solving scientific problems.
brief description of the course in Estonian
Kursus pakub teadmisi ja oskusi mõistamaks, disainimaks, kasutamaks ja haldamaks kaasaegseid masinõppelahendusi teaduslike probleemide lahendamiseks.
Kursus tutvustab kõrgtaseme tööriistu süvaõppe algoritmide kasutamiseks. Üliõpilased õpivad erinevate süvaõppemeetodite kasutamist ja tööpõhimõtteid. Õpitakse ka tulemuste valideerimist ja interpreteerimist. Samuti käsitletakse tehnoloogilisi ja praktilisi ohtusid, mis võivad kaasneda süvaõppemeetodite rakendamisega.
brief description of the course in English
The course offers knowledge and skills to understand, design, use and manage contemporary solutions of deep learning to handle scientific problems. The course introduces high level tools to use algorithms of deep learning. Students learn usage and working principles of different methods of deep learning. They also study the validation and interpretation of results. Moreover, they learn technological and practical risks that may occur in the application of methods of deep learning.
type of assessment in Estonian
-
type of assessment in English
-
independent study in Estonian
-
independent study in English
-
study literature
1. A. Géron, Hands-On Machine Learning with Scikit-Learn & TensorFlow. O’Reilly Media, Inc., 2017.
2. M. Erdmann et al, Deep Learning For Physics Research. World Scientific, 2021.
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
2.0
practices
-
exercises
0.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus or link to Moodle or to home page
2024/2025 autumn
Nataliia Kinash, LT - Department of Cybernetics
English
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    2023/2024 autumn
    Nataliia Kinash, LT - Department of Cybernetics
    English
      2022/2023 autumn
      Nataliia Kinash, LT - Department of Cybernetics
      English
        Course description in Estonian
        Course description in English