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

Lecture 11 (I)

Scalable private learning with PATE

Zehao Li

Lecture 11 (II)

Differentially private fair learning

Burak Varici

Lecture 12 (I)

On sampling, anonymization, and differential privacy or, k- anonymization meets differential privacy

Charlie Cook

Lecture 12 (II)

Evaluating differentially private machine learning in practice

Sharmishtha Dutta

Lecture 13

Robustness, robust training, certified defense, robust optimization, adversarial examples, black-box attacks

Lecture 14

Poisoning attacks against support vector machines

Arpan Mukherjee

Lecture 15

Manipulating machine learning: Poisoning attacks and countermeasures for regression learning

Dong Hu

Lecture 16

Explaining and harnessing adversarial examples

Vijay Sadashivaiah

Lecture 17 (I)

Practical black-box attacks against machine learning

Alex Mankowski

Lecture 17 (II)

A robust meta-algorithm for stochastic optimization

Bao Pham

Lecture 18

Mitigating unwanted biases with adversarial learning

Kara Davis

Lecture 19

Fairness, fairness measures, counterfactuals, fair representation, certified fairness, bias mitigation, fair classification

Lecture 20

Equality of opportunity in supervised learning

Matthew Youngbar

Lecture 21 (I)

Fairness through awareness

Farhad Mohsin

Lecture 21 (II)

Learning fair representations

Farhad Mohsin

Lecture 22 (I)

Counterfactual fairness

Rhea Banerjee

Lecture 22 (II)

Fairness constraints: Mechanisms for fair classification

Rhea Banerjee

Lecture 23

Transparency, explainability, trust, transparency, interpretability

Lecture 24 (I)

Towards a rigorous science of interpretable machine learning

Zirui Yan

Lecture 24 (II)

Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems

Zirui Yan

Lecture 25 (I)

A unified approach to interpreting model predictions

Lucky Yerimah

Lecture 25 (II)

Why should I trust you? Explaining the predictions of any classifier

Lucky Yerimah

Lecture 26

Explaining explanations in AI

Andrew Nguyen