Deep Learning and Advanced AI Techniques cover the basics of deep learning and advanced AI techniques and recent technological trends. It also includes a few aspects of generative AI. The course covers:

• Fundamentals of artificial intelligence
• Reinforcement learning – Learning to play games and beyond
• Fundamentals of deep learning, network design, and training
• Transfer learning and pre-trained models
• Data augmentation and synthesis
• Core ideas of: Graph Neural Networks, Autoencoders, Generative adversarial networks (GANs), recurrent neural networks, convolutional neural networks, diffusion models
• Explainability and interpretability in AI
• Case studies and applications in various industries and for various tasks
• Recent trends and future directions in AI and deep learning

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.

After successful completion of the course, students will

Professional competence
• understand the basic concepts and methods of artificial intelligence and deep learning
• be able to identify suitable applications for artificial intelligence and deep learning
• understand key concerns in adopting and leveraging artificial intelligence

Methodological competence
• select, use, and adjust existing models and methods for a given task or data set

Personal competence
• critically reflect on analytical outcomes
• be able to improve and mitigate self-inflicted errors

Technological competence
•be able to use a deep learning framework such as Keras

Written exam