This course covers core topics in machine learning theory, including generalization bounds in the offline setting (including VC dimension, one-inclusion graphs, compression schemes) and regret bounds in the online setting (including multiplicative weights, Littlestone dimension). We will also explore advanced topics such as adversarial robustness, the intersection of game theory and learning, etc.