Deep Learning for Science

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.

NB! This course will take place in autumn semester 2025/2026 which starts on 1st of September and ends on 25th of January (you can find that information under Start date section). TalTech's timetables for Autumn semester 2025 will be published at the end of June via tunniplaan.taltech.ee. Switch the page to English and use "Search" and "Open detailed search" to find your course. NB! Some courses are taught by several lecturers during the same semester. Make sure that the course name and lecturer/teacher infromation of your course match with the information given in the Course Catalogue.

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.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

• 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.

lectures, practices