Foundations of Deep Learning

Course type: Lecture + Exercise
Time: Wednesday, 12:15 – 13:45, first meeting: Oct. 20
Location: The course will be fully virtual/online.
Weekly flipped classroom sessions will be held on Zoom.

See ILIAS for Zoom link.

Organizers: Frank Hutter, Abhinav Valada, Steven Adriaensen, Samuel Müller, Yash Mehta, Niclas Vödisch
Web page: ILIAS (please also register for all elements of this course module in HISinOne)

Foundations of Deep Learning

Deep learning is one of the fastest growing and most exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.

Course Overview

The course will be taught in English and will follow a flipped classroom approach. Every week there will be:
– a video lecture
– an exercise sheet
– a flipped classroom session (virtual/online, Wednesdays 12:15 – 13:45)
– an attendance optional exercise session (in-class/offline, Thursdays 10:15 – 11:45)
At the end, there will be a written exam (likely ILIAS test).
Exercises must be completed in groups and must be submitted a week (+ 1 day) after their release. Your submissions will be graded and you will receive weekly feedback. Your final grade will be solely based on a written examination, however a passing grade for the exercises is a prerequisite for passing the course.
Online course: All material will be made available online and course participation will not require in-person presence. That being said, we offer the opportunity for direct interaction with our tutors during weekly attendance optional in-class exercise sessions.
Exam: The exam will likely be a test you complete on ILIAS. In person presence may be required (tba).

Course Schedule

The following are the dates for the flipped classroom sessions:
20.10.21 – Kickoff: Info Course Organisation / Team Formation
27.10.21 – Week 1: Overview of Deep Learning
03.11.21 – Week 2: From Logistic Regression to MLPs
10.11.21 – Week 3: Backpropagation
17.11.21 – Week 4: Optimization
24.11.21 – Week 5: Regularization
01.12.21 – Week 6: Convolutional Neural Networks (CNNs)
08.12.21 – Week 7: Recurrent Neural Networks (RNNs)
15.12.21 – Week 8: Attention and Transformers
22.12.21 – Week 9: Practical Methodology & Architectures
12.01.22 – Week 10: Hyperparameter Optimization
19.01.22 – Week 11: Neural Architecture Search
26.01.22 – Week 12: Auto-Encoders, Variational Auto-Encoders, GANs
02.02.22 – Week 13: Uncertainty in Deep Learning
09.02.22 – Round-up / Exam Q & A

The course material (lecture video, slides, exercise sheet) for “Week N” will be made available a week before the flipped classroom session for “Week N”. For example, the material for Week 1 will be available on 20.10.21 and solutions to the exercises must be submitted latest 28.10.21 at 23:59. We will be using Zoom and the meeting link can be found on ILIAS in the “Flipped Classroom” folder.

In the first session (on 20.10.21) you will get additional information about the course and get the opportunity to ask general questions (and form groups!) While there is no need to prepare for this first session, we encourage you to already think about forming teams. The last flipped classroom session is held on 09.02.22.


If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer).
Alternatively, you can also email