A preliminary structure is given below:
Lecture 1. Introduction
Lecture 2. Probably Approximately Correct Framework and Empirical Risk Minimization
Lecture 3. Concentration inequalities
Lecture 4. The Vapnik-Chervonenkis (VC) Theory
Lecture 5. Linear Classification and Regression
Lecture 6. Regularization, Stability and Optimization
Lecture 7. Support Vector Machines and Kernel Methods
Lecture 8. Deep neural networks
Lecture 9. Clustering. Cluster validation and algorithms
Lecture 10. Reinforcement learning: model-free vs model-based algorithms
Lecture 11. Reinforcement learning: function approximation and deep RL