AI Systems

Course overview

Modern AI Systems: AI Systems and Machine Learning Explore the interface between Artificial Intelligence (AI) and Machine Learning (ML) in modern AI-driven systems. This course covers important AI models, machine learning techniques, and real-world AI applications across various industries. Introduction to AI Systems Understanding AI-driven automation and decision-making The role of AI systems in modern industries AI vs. traditional software systems Fundamentals of Machine Learning in AI Supervised and unsupervised learning techniques Key ML algorithms: classification, regression, clustering Evaluating ML models for AI applications Neural Networks and Deep Learning Artificial Neural Networks (ANNs) and the basics of deep learning Convolutional Networks (CNNs) for image recognition Recurrent Neural Networks (RNNs) for time series and NLP AI Systems and Natural Language Processing (NLP) AI-driven text and sentiment analysis Transformer models and language generation AI chatbots and automated virtual assistants Optimizing AI Systems for Better Performance Hyperparameter tuning and AI model refinement Cloud-based AI model deployment strategies Dealing with scalability and ethical AI considerations The Future of AI Systems and Emerging Trends Generative AI and the rise of large language models AI in automation, robotics, and predictive analytics Ethical AI development and responsible AI implementation The course explores advanced techniques in AI systems and machine learning, including transformer models and cloud-based AI deployment strategies.

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Aachen · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Bonn · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Dresden · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-16T23:00:00.000Z · Duisburg · Combined Learning