Universität Wien

580012 SE SE Deep-learning topics in the field of computer-aided drug design (2024S)

1.50 ECTS (1.50 SWS), SPL 58 - Doktoratsstudium Pharmazie, Ernährungswissenschaften und Sportwissensch
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

Language: German

Lecturers

Classes

The course will be held in English!

The course will kick off on Wednesday, 13th March 2024, in room 2D313, adopting a bi-weekly schedule, with a special introductory session on deep learning principles scheduled to accommodate all participants' needs. Please note the updated session timings and days as follows:

March 13, 2024: Course Introduction, 14:45 to 16:00, Wednesday, Room 2D313
March 22, 2024: Introduction into Deep Learning, 10:00 to 11:00 AM, Special Session
March 27, 2024: Regular Session, 15:15 to 16:30, Wednesday
April 9, 2024: Regular Session, 15:15 to 16:30, Tuesday
April 23, 2024: Regular Session, 15:15 to 16:30, Tuesday
May 8, 2024: Regular Session, 15:15 to 16:30, Wednesday
May 22, 2024: Regular Session, 15:15 to 16:30, Wednesday
June 5, 2024: Regular Session, 15:15 to 16:30, Wednesday
June 19, 2024: Regular Session, 15:15 to 16:30, Wednesday
July 2, 2024: Course Conclusion and Review, 15:15 to 16:30, Tuesday

Information

Aims, contents and method of the course

This course is designed to provide participants with an in-depth understanding of a specific deep-learning topic relevant to computer-aided drug design. The goal is to explore and gain a comprehensive understanding of a particular theme, which will be determined at the beginning of the course. Throughout the semester, we will start with an introductory session and progressively delve deeper, incorporating discussions on pertinent publications to enrich our learning. By the end of the semester, participants will have acquired a solid grasp of the chosen topic.

Potential Topics:

Geometric Deep Learning
Uncertainty Estimation
Learning Strategies
Explainable AI
Participants are encouraged to suggest additional topics of interest during the first session for group consideration.

Assessment and permitted materials

Attendance and active participation (You are allowed to miss one lecture)
Each participant is required to prepare and deliver at least one presentation during the course, facilitating a deeper dive into specific aspects of the selected topic.

Minimum requirements and assessment criteria

This course is tailored for students, researchers, and professionals with an interest in deep learning applications within the drug design field. While participants with a basic understanding of deep learning concepts will find the course particularly beneficial, individuals without prior deep learning knowledge are also welcome to enroll. We acknowledge the diversity in participants' backgrounds and aim to make the course inclusive and accessible to all interested parties. To accommodate those new to deep learning, we will dedicate time during the first session to discuss the foundational knowledge required for the course. Additionally, an intermediate session focused on an introduction to deep learning concepts is scheduled for Friday, 22nd March 2024, from 10:00 to 11:00 AM, bridging the gap between the first and second sessions. This extra session is designed to ensure all participants are well-prepared to engage with the course material fully.

Examination topics

Reading list

Association in the course directory

Last modified: Mo 11.03.2024 11:08