Statistical Topics in Modern Machine Learning (FGV EMAP, Rio)

Schedule

From April, 15th to June, 15th

• • Tuesdays 9:20am - 11am, Auditório 318
• • Tuesdays 4:20pm - 6pm, Sala 1014
• • Fridays 2:20pm - 4pm, Sala 1014

Lecture Notes

Written in collaboration with Claire Boyer, Ismaël Castillo and Étienne Roquain.

1 - Starter on neural networks
2 - Approximation properties of neural networks
3 - Complexity of neural networks
4 - Regression with neural networks , ERM and minimax lower bounds
5 - Generative adversarial networks
6 - Diffusion models
7 - Confidentiality and privacy-preserving inference
8 - Conformal inference
9 - Reproducing kernel Hilbert spaces, Neural tangent kernel
10 - Double descent and benign overfitting