Theoretical advances in deep learning (Master seminar)

Pre-course meeting

The pre-course meeting will be held online on BBB under this link on 08.02.2021, 18:00 - 19:00.

We will use this meeting to discuss the focus of the seminar and the structure in more detail.

You can find a recording of the meeting here and the slides with more detailed information here.

Focus of the seminar

Neural networks, particularly deep networks, have achieved unprecedented popularity over the past decade. While the empirical success of neural networks has reached new heights, one of the major achievements in recent years has been new theoretical studies on the statistical performance of neural networks. 
This seminar will look at the following important topics on neural networks from a mathematical perspective:

  • Generalization error for neural networks and related concepts from learning theory
  • Optimization and convergence rates for neural networks
  • Sample complexity and hardness results
  • Connection of deep learning to other learning approaches (kernel methods etc)
  • Robustness of neural networks

Several recent papers from top machine learning conferences will be discussed during the seminar.


  • Prior knowledge of Machine learning (IN2064 or equivalent) is mandatory. 
  • Experience of Statistical foundations of learning (IN2378); Introduction to deep learning (IN2346) will be preferred


Upon completion of this module, the students will:

  1. have acquired knowledge on the current trends in deep learning theory.
  2. be familiar with recent theoretical works from top machine learning conferences
  3. be able to apply mathematical tools to analyze performance of neural networks


Each student will be allotted a research paper. The student will have to submit a report/review on the paper (submission deadline in the middle of the semester). Additionally, there will be presentations will be held together as a block seminar. The slides have to be submitted few weeks before the presentation.
The final grades will depend on the presentation (60%) and a written report (40%).