The WS22/23 course will be offered by Prof. Dai. Please refer to the course page of WS22/23!
Welcome to the Introduction to Deep Learning course offered in SS22.
The first event in the semester will be an on-site exercise session where we will announce all remaining details of the lecture. The session will take place on Monday, 25.04.2022 from 14:00 c.t., at MI HS 1, Friedrich L. Bauer Hörsaal (5602.EG.001).
The lectures will start in the week of May 02, 2022. All lectures will be on-site, but recordings will be made available online. They will be streamed and uploaded as a video in RGB Live. To access the videos please check out the link we provided in Moodle.
Date and location: Monday 2pm-4pm, MI HS 1, Friedrich L. Bauer Hörsaal
Lecturers: Prof. Dr. Laura Leal-Taixé.
ECTS: 6
SWS: 4
The tutorial consists of weekly exercise coding tasks and tutorial videos. We will upload the exercises and slides on our website and moodle. To access the tutorial videos, please use the link provided in Moodle. In addition, we are going to provide on-site Q&A sessions on a regular basis. You can find a tentative schedule of the on-site Q&A sessions below.
Date and location: Tuesday 2pm-4pm, online (except on-site Q&A sessions, find schedule below)
Lead Teaching Assistants: Andreas Rössler and Franziska Gerken.
Student Teaching Assistants: Julian Balletshofer, Tathagata Bandyopadhyay, Wei Cao, Zeynep Gerem, Dan Halperin, Quoc Trung Nguyen, Dominik Schmauser, Anna Weber
The lecture consists of weekly on-site lectures as well as weekly exercise coding tasks and tutorials (solution presentations). In addition, we are going to provide on-site Q&A sessions on a regular basis. Lectures will take place on mondays and the on-site Q&A sessions will take place tuesdays (see planned schedule below).
The weekly exercises and tutorial videos will be published Tuesdays at 4pm. The deadline for each exercise will be one week later, on Monday at 11.59pm. Exercises will be submitted at our submission system.
The first exercise will be published on Tuesday, 26.04.22 at 4pm. This exercise will be a first experience with our submission system and a python refresher. The deadline for this exercise is set to Monday, 16.05.22, 11.59pm. The submission system will be available on 27.04.22.
Exercise Schedule Tutorial videos as well as exercises will be uploaded Tuesdays.
Tentative Dates for the on-site Q&A Sessions The Q&A sessions will take place at Galileo Audimax (stream/vod as usual).
In addition to the tutorial we also offer to discuss questions in person. This semester, the office hours will be held via TUM-Zoom. The zoom links for the office hours will be announced on moodle.
The office hours will start in the second week (May 02, 2022).
The final exam date is TBD. The exam will most likely be a traditional onsite exam, so please take this into consideration if you intend to receive credits for this class. Credits are only awarded for students that participated and successfully passed the exam.
As this course is taught every semester, there will be no retake exam; you will have to take next semester’s exam (bonus will be transferred).
We will use Moodle for discussions, publication of office hour links and to distribute exam related information. The slides and all material will be posted and maintained on Moodle, and additionally uploaded here on the website.
We want to provide access to our lecture for as many students as possible. If you are affiliated with TUM (e.g. LMU student, Ph.D. student, TUM student who cannot register for courses yet but have a TUM token, etc.), we will add you to our class manually. Please fill out this form if you belong to one of the above mentioned group.
If you are a student from another university or simply interested in Deep Learning, you can access the lectures and exercises through this webpage which will be updated accordingly. All notebooks feature separate tests where you can record your performance, though you will not be able to access to submission website.
If you have any questions regarding the organization of the course, do not hesitate to contact us at: i2dl@dvl.in.tum.de. Please refrain from using the personal email addresses.
For questions on the syllabus, exercises or any other questions on the content of the lecture, we will use the forum discussion board.
This class will be offered next semester (WS22/23) as well. Exercise bonus can be transfered.
