Lecture 01

Introduction to trustworthy ML

Lecture 02

ML overview (common ML models, optimization, ML procedures, SGD)

Lecture 03

ML overview (practical aspects of ML, optimization techniques, NNs, back propagation, Pytorch)

Lecture 04

Attacks and adversaries, data inference attacks, membership inference, white-box attacks, information leakage

Lecture 05

Membership inference attacks against machine learning model

Arif Huzaifa

Lecture 06

Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Roman Vakhrushev

Lecture 07 (I)

Information Leakage in Embedding Models

Ryan Kaplan

Lecture 07 (II)

CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel

Alex Sidgwick

Lecture 08 (I)

Exploring connec- tions between active learning and model extraction

Anmol Dwivedi

Lecture 08 (II)

High Accuracy and High Fidelity Extraction of Neural Networks.

M. Shahid Modi

Lecture 09

Introduction to privacy, differential privacy, private distributed learning, privacy evaluation

Lecture 10

Deep learning with differential privacy

Momin Abbas