Lecture 1: Basic of AI: What are DNN and how they work?
Lecture 2: The design of an AI project: data, annotations, validation, error metrics
Lecture 3: Case Study – AI for Pathology from A to Z
Lecture 4: Take your annotations to the maximum ! How AI can help you create high quality and huge annotated data sets.
Lecture 5: Supervised, Weakly Supervised, Selfsupervised – what do they mean and what are they useful for?
Lecture 6: XAI – can we understand how AI makes decisions?
