The course will cover classical Machine Learning techniques and present examples of their application in oncology. At first, it lays the foundations of ML and a broad description of the basic techniques in supervised, unsupervised, and reinforcement learning. Once the techniques have been presented, their application to some case studies is analysed, and a final hands-on experience will give the student the opportunity to apply ML techniques in real-world settings.
Lecture 1: Introduction to Machine Learning for Decision Support
Lecture 2: Unsupervised Techniques
Lecture 3: Reinforcement Learning
Lecture 4: Explainable AI
Lecture 5: ML Canvas