inf5400 - Advanced Topics in Applied Deep Learning

inf5400 - Advanced Topics in Applied Deep Learning

Department of Computing Science 6 KP
Module components Semester courses Winter semester 2024/2025 Examination
Lecture
  • Unlimited access 2.01.5400 - Deep Unsupervised Learning Show lecturers
    • Prof. Dr. Nils Strodthoff
    • Juan Lopez Alcaraz
    • Tiezhi Wang

    Thursday: 08:00 - 10:00, weekly (from 17/10/24)
    Friday: 14:00 - 16:00, weekly (from 18/10/24)
    This lecture encompasses two primary subjects: self-supervised learning and modern generative models. In the first part, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. In the second part, we will explore applications of these principles to specific data modalities such as computer vision, natural language processing (including an extensive coverage of large language models) and audio/time series. Finally, the third part will focus on generative models, where we will cover a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and (latent) diffusion models.
Exercises
  • Unlimited access 2.01.5400 - Deep Unsupervised Learning Show lecturers
    • Prof. Dr. Nils Strodthoff
    • Juan Lopez Alcaraz
    • Tiezhi Wang

    Thursday: 08:00 - 10:00, weekly (from 17/10/24)
    Friday: 14:00 - 16:00, weekly (from 18/10/24)
    This lecture encompasses two primary subjects: self-supervised learning and modern generative models. In the first part, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. In the second part, we will explore applications of these principles to specific data modalities such as computer vision, natural language processing (including an extensive coverage of large language models) and audio/time series. Finally, the third part will focus on generative models, where we will cover a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and (latent) diffusion models.
Notes on the module
Prerequisites

This module is intended for an advanced audience and requires a solid understanding of the fundamentals of Machine Learning. Experience in training deep neural networks is essential in this context.
Prüfungszeiten

At the end of the lecture period
Module examination

Written exam / oral exam / project work
Skills to be acquired in this module

Professional competence
The students

  • have in-depth knowledge of selected application areas of deep learning. They are familiar with various solutions for problems in these areas, know their advantages and disadvantages, and can practically implement them and adapt them to their own issues.

Methodological competence
The students

  • independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature.

Social competence
The students

  • can present solution approaches for problems in this area to the plenary and defend them in discussions.

Self-competence
The students

  • are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content.

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