Course type:	Lecture + Exercise
Time:	Lecture: Monday 14:15 - 15:45; Exercise: Thursday 10:00 - 11:30
Location:	Lecture: Building 101, HS 00 026 (μ-SAAL); Exercise: Building 082, HS 00 006 (KinoHörsaal)
Organizers:	Frank Hutter, Joschka Bödecker, Abhinav Valada, Arber Zela, Raghu Rajan, Jörg Franke, Andreas Sälinger
Web page:	, ILIAS

Foundations of Deep Learning

Deep learning is one of the fastest growing and 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 following are the dates for the in-class discussions (The video lectures will be released on the Tuesday the week earlier): 21.10.19 - Week 1: Overview of Deep Learning
04.11.19 - Week 2: From Logistic Regression to MLPs
11.11.19 - Week 3: Backpropagation
18.11.19 - Week 4: Optimization
25.11.19 - Week 5: Regularization
02.12.19 - Week 6: Convolutional Neural Networks (CNNs)
09.12.19 - Week 7: Recurrent Neural Networks (RNNs)
16.12.19 - Week 8: Practical Methodology & Architectures
13.01.19 - Week 9: Hyperparameter Optimization & Neural Architecture Search
20.01.20 - Week 10: Uncertainty in Deep Learning
27.01.20 - Week 11: Auto-Encoders, Variational Auto-Encoders, GANs
03.02.20 - Week 12: Group Presentations; Project Kickoff
The course will be taught in english and we will follow a flipped classroom approach.