Machine and Deep Learning

Course overview

The course provides a detailed examination of the field of Machine Learning - here, artificial knowledge is generated from experience. Initially, you will learn the fundamentals, followed by the two categories of supervised and unsupervised learning, as well as the topic of evaluation and improvement. Subsequently, the course explains the methods of Deep Learning based on neural networks. Deep Learning, as a subfield of Machine Learning, utilizes algorithms for intelligent learning processes. Related tools, image classification using Convolutional Neural Networks (CNN), and later Recurrent Neural Networks (RNN) and Generative Models will be taught. Machine Learning Introduction to Machine Learning Supervised Learning Unsupervised Learning Evaluation and Improvement Project Work in Machine Learning Deep Learning Machine Learning: Deep Learning Fundamentals of Neural Networks Convolutional Neural Network (CNN) Transfer Learning Regional CNN Methods of Creative Image Generation Recurrent Neural Networks Text Processing with Neural Networks Language Models Deep Reinforcement Learning Bayesian Neural Networks Project Work in Deep Learning Changes are possible; the course content is regularly updated.

Upcoming dates

• 2026-09-20T22:00:00.000Z · 2026-11-12T23:00:00.000Z · Aachen · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-11-12T23:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-11-12T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-11-12T23:00:00.000Z · Darmstadt · Combined Learning
• 2026-09-20T22:00:00.000Z · 2026-11-12T23:00:00.000Z · Dortmund · Combined Learning

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