HSG_Logo_EN_RGB

Korbinian Riedhammer

Course location

Home university

University of St.Gallen
Nuremberg Tech University

Course location

University of St.Gallen

Home university

Nuremberg Tech University
Riedhammer
Prof. Dr. Korbinian Riedhammer earned his diploma and doctoral degrees in computer science (Diplom Informatik, Doktor-Ingenieur) at the University of Erlangen-Nürnberg, Germany, in 2007 and 2012, respectively. His dissertation is focused on automatic speech recognition and understanding, and how such technologies can be used to extract knowledge from spoken documents. From 2012-2013, he was a postdoctoral visiting scientist at the International Computer Science (UC Berkeley) working on low-resource methods for keyword search in the context of the IARPA Babel program. In 2014, he cofounded Remeeting (now Mod9 Technolgies), a venture-backed startup that provides highly customizable on-premise solutions for speech recognition and indexing. In 2016, he returned to Germany as a tenured full professor for computer science at the Technische Hochschule Rosenheim. Since 2019, he is professor for software architecture and machine learning at the Technische Hochschule Nürnberg, where he also serves as the dean of students in the CS department.

Courses taught by this instructor

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Next course

Location

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Location

Next course

Deep Learning: Fundamentals and Applications

Course content: – Machine Learning Refresh o Supervised Learning vs. Unsupervised Learning o Traditional Machine Learning vs. End-to-End Learning – Fundamentals of Neuronal Networks: o Rosenblatt Perceptron and Neurons o Network Structure (feed-forward, recurrent), matrix notation, forward evaluation – Training as optimization o Loss and Error functions o Backpropagation o SGD and other optimizer – Activation functions and topologies o Convolutional neural networks o Generative Adversarial Networks o Long short-term memory networks o Special layer types (inception, resnet) o Embeddings o Attention Mechanis & Transformer – Applications to real-world problems: o Acoustic keyword recognition (audio/speech processing) o Sentiment analysis (text processing) o Digit recognition (image processing) o Tiny Image Recognition (image processing) o Face Detection and Tracking (image/video processing) o Stock market prediction (time series prediction) – Training on large data sets (Hardware, GPU) – Trustworthy AI Structure: The course is a theoretical content in the morning and practical exercises in the afternoon in form of lab Jupyter notebook programming.
...

...

M

2023

Deep Learning: Fundamentals and Applications

Course content: – Machine Learning Refresh o Supervised Learning vs. Unsupervised Learning o Traditional Machine Learning vs. End-to-End Learning – Fundamentals of Neuronal Networks: o Rosenblatt Perceptron and Neurons o Network Structure (feed-forward, recurrent), matrix notation, forward evaluation – Training as optimization o Loss and Error functions o Backpropagation o SGD and other optimizer – Activation functions and topologies o Convolutional neural networks o Generative Adversarial Networks o Long short-term memory networks o Special layer types (inception, resnet) o Embeddings o Attention Mechanis & Transformer – Applications to real-world problems: o Acoustic keyword recognition (audio/speech processing) o Sentiment analysis (text processing) o Digit recognition (image processing) o Tiny Image Recognition (image processing) o Face Detection and Tracking (image/video processing) o Stock market prediction (time series prediction) – Training on large data sets (Hardware, GPU) – Trustworthy AI Structure: The course is a theoretical content in the morning and practical exercises in the afternoon in form of lab Jupyter notebook programming.
...

...