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Damian Borth

Course location

Home university

University of St.Gallen
University of St. Gallen

Course location

University of St.Gallen

Home university

University of St. Gallen
borth-damian
Damian Borth is Full Professor of Artificial Intelligence and Machine Learning at the University of St.Gallen, Switzerland. Formerly, he was Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI. Damian‘s research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights from online media streams. Damian currently serves as a member of the steering group at the VolkswagenStiftung, the review committee at the Baden-Wurttemberg Stiftung, the assessment committee for the Investment Innovation Benchmark (IIB) and several other steering- and program committees of international conferences and workshops. Damian did his postdoctoral research at UC Berkeley and the International Computer Science Institute (ICSI) in Berkeley. He received his PhD from the University of Kaiserslautern and the German Research Center for Artificial Intelligence (DFKI). During that time, Damian stayed as a visiting researcher at the Digital Video and Multimedia Lab at Columbia University.

Courses taught by this instructor

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Next course

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Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Location

Next course

Deep Learning: Fundamentals and Applications

Over the last decade, Artificial Intelligence (AI) has seen a steep rise to the top, with large tech companies such as Google, Facebook, and Amazon investing heavily in research and development. From chatbots and face recognition algorithms to self‐driving cars, AI quickly transforms technology, business, and society. The driving factors behind this momentum are the recent advances in machine learning, particularly deep learning.
This course introduces the fundamental concepts of Deep Learning (DL). The objective is to provide a broad overview of the field, empowering you to understand the relationship between AI and DL and DL’s exciting and challenging application areas. Upon completing this course, you should be familiar with the common terminology in the field and understand its basic concepts. In addition, you will learn how distinct DL architectures can be applied to train your machine learning models. The detailed topics covered in the course include:
• Machine Learning Introduction
• Artificial Neural Networks (ANNs) and Backpropagation
• Convolutional Neural Networks (CNNs) and Autoencoders (AENs)
• Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs)
• Real-World Challenges and Trustworthy AI

We look forward to embarking on this exciting journey with you, exploring the cutting-edge concepts that are reshaping our technological landscape.
...

...

M

2024

Deep Learning: Fundamentals and Applications

Over the last decade, Artificial Intelligence (AI) has seen a steep rise to the top, with large tech companies such as Google, Facebook, and Amazon investing heavily in research and development. From chatbots and face recognition algorithms to self‐driving cars, AI quickly transforms technology, business, and society. The driving factors behind this momentum are the recent advances in machine learning, particularly deep learning.
This course introduces the fundamental concepts of Deep Learning (DL). The objective is to provide a broad overview of the field, empowering you to understand the relationship between AI and DL and DL’s exciting and challenging application areas. Upon completing this course, you should be familiar with the common terminology in the field and understand its basic concepts. In addition, you will learn how distinct DL architectures can be applied to train your machine learning models. The detailed topics covered in the course include:
• Machine Learning Introduction
• Artificial Neural Networks (ANNs) and Backpropagation
• Convolutional Neural Networks (CNNs) and Autoencoders (AENs)
• Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs)
• Real-World Challenges and Trustworthy AI

We look forward to embarking on this exciting journey with you, exploring the cutting-edge concepts that are reshaping our technological landscape.
...

...

Generative AI for Text, Audio & Images

We will cover the basic concepts of prominent methods for generative deep learning before starting a deep dive on their application to text, audio and images. Since we will focus on applications and the usage of respective foundation models and toolkits, we strongly recommend you get familiar with deep learning ahead of this course. In detail, we will cover:

Theory: Prominent generative Deep Learning Methods
-Generative Pretrained Transformers (GPT), Fine-Tuning, RLHF, Instruction Learning, Zero-Shot Learning, In-Context Learning, Chain-of-Thought
-Generative Adversarial Networks (GAN)
-Variational Auto-Encoders (VAE)
-Diffusion
-Style transfer

Hands-On:
-Text: GPT Prompt Engineering
-Text to Speech, Voice Conversion
-Image generation and captioning


We will conclude the course with an outlook on risks, limitations, ethical and legal implications.
...

...

M

2024

Generative AI for Text, Audio & Images

We will cover the basic concepts of prominent methods for generative deep learning before starting a deep dive on their application to text, audio and images. Since we will focus on applications and the usage of respective foundation models and toolkits, we strongly recommend you get familiar with deep learning ahead of this course. In detail, we will cover:

Theory: Prominent generative Deep Learning Methods
-Generative Pretrained Transformers (GPT), Fine-Tuning, RLHF, Instruction Learning, Zero-Shot Learning, In-Context Learning, Chain-of-Thought
-Generative Adversarial Networks (GAN)
-Variational Auto-Encoders (VAE)
-Diffusion
-Style transfer

Hands-On:
-Text: GPT Prompt Engineering
-Text to Speech, Voice Conversion
-Image generation and captioning


We will conclude the course with an outlook on risks, limitations, ethical and legal implications.
...

...