Introduction to Deep Learning (I2DL) (IN2346)

Welcome to the Introduction to Deep Learning course offered in SoSe 24.

General Course Structure

Lectures: Prerecorded, uploaded every Tuesday, 10:00 a.m.
Tutorials: Prerecorded, uploaded every Thursday, 10:00 a.m.
Exercises: Mostly coding, jupyter-notebooks and Python based, uploaded every Thursday, 10:00 a.m.

If you are a student from another university or simply interested in Deep Learning, you can access the lecture and exercise videos 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. We will also provide solutions and discussions about the submissions in our weekly exercise videos which are all publicly hosted on this website.

For details about the exercises, please refer to the first tutorial session. In particular, you will submit your solutions via our submission website (https://i2dl.vc.in.tum.de).

Lecture Slides and Recordings

Lecturer: Prof. Dr. Matthias Niessner

ECTS: 6

SWS: 4

Tutorial Slides and Recordings

Exercises' Files

Exercises' Solutions

Office hours

In addition to the tutorial we also offer to discuss questions in person. This semester, the office hours will be held via TUM-Zoom. Please keep the following in mind to ensure the office hours remain helpful for everyone:

• Mute your microphone when entering a zoom-session
• Wait patiently until it’s your turn, the TAs will try to answer the questions in the correct order (just like in the usual office hours)

Office hours start on the second week of the class (22.04.2024 onwards). For one hour, you can have direct contact with one of our TAs to resolve your issues in person.

Final Exam and Credits

The final exam will be on 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 provide a mock exam and the solution for your reference.

Prerequisites

• Strong mathematical background: linear algebra, calculus.
• Previous knowledge of Python is needed. This includes scripting skills such as operating system calls.
• Machine Learning background is recommended.

Forum

We will use Piazza for discussions, publication of the office hour zoom link and to distribute exam related information. We will not be using Moodle. The slides and all material will also be posted and maintained on Piazza, but it will be identical the material hosted on this website.

TUM students can register directly with TUM email address. The other students (e.g. LMU student) need to manually send their email address to us to get enrolled on Piazza.

Contact us

If you have any questions regarding the organization of the course, do not hesitate to contact us at: i2dl@vc.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.