Introduction to Deep Learning (I2DL) (IN2346)

Welcome to the Introduction to Deep Learning course offered in WiSe 25/26.

Announcements

• No lecture on 21st October

• Form for external students registration has been added below

General Course Structure

Lectures: Tuesday 14:00-16:00, MI HS1 and live stream for TUM students (link on Piazza), recordings will be available on TUMLive
Tutorials: Prerecorded, uploaded every Thursday, 10:00 a.m.
Exercises: Mostly coding, jupyter-notebooks and Python based, uploaded every Thursday, 10:00 a.m.

The exercise will be used to determine a bonus for the exam. Please watch the first tutorial video where we will cover the class structure and planning in more detail. In particular, you will submit your solutions via our submission website. The exercise submissions will start in the first week of the semester. See below, if you are a LMU or TUM student that currently can't sign up for the class regularly via TUM online.

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.

Lecture Slides and Recordings

Lecturer: Prof. Dr. Matthias Niessner

ECTS: 6

SWS: 4

• Week 1 - Lecture 1: Introduction to the lecture, Deep Learning, Machine Learning and submission system

Tutorials and Exercises

Tutorial and Exercise release date: Thursdays, 10:00

Exercise submission date: following Wednesday, 23:59

Lead Teaching Assistants: Chandan Yeshwanth and Haoxuan Li and Yujin Chen

Tutorial Slides and Recordings

• Week 1 - Tutorial 1 Slides | Tutorial 1 Recording

Exercises' Files

• Week 1 - Exercise 1

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:

• Check out the correct zoom link of the time-slot
• 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. For one hour, you can have direct contact with one of our TAs to resolve your issues in person. The links to these office hours can be accessed via the forum below and is only available to registered students.

Final Exam and Credits

The final exam will be on TBD. The exam will be an onsite written 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. (will be uploaded during the semester)

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.