ECE 885 F20

Course Description

In recent years, Machine Learning (ML) has made significant advancements across various domains, accomplishing impressive performance levels that were previously thought to be unattainable. ML has demonstrated its capabilities in applications such as healthcare, retail marketing, earthquake detection, machine translation, text-to-speech conversion, object recognition, and even self-driving cars. However, the widespread implementation of machine learning models in real-world scenarios has opened up new avenues for cyber-security threats. It is crucial to consider the security and privacy implications associated with these developments. This course aims to explore state-of-the-art technologies that ensure privacy-preserving AI. Participants will gain insights into cutting-edge methodologies designed to protect sensitive information in AI systems. Additionally, the course will delve into utilizing ML techniques to enhance system security, as well as understanding how ML can be used both for launching attacks and developing effective countermeasures. By examining the intersection of machine learning, security, and privacy, participants will develop a comprehensive understanding of the challenges and opportunities present in this rapidly evolving field.

Learning Outcomes

1. Learn the cutting-edge technologies for machine learning.

2. Explain some of the applications of different machine learning models and relation to cybersecurity.

3. Explain attacks on machine learning models and countermeasures.

4. Explain how to appropriately build and train different machine learning model.

5. Explain how machine learning models can be used to launch attacks (Adversarial Learning) and counter security threats.

6. Apply machine learning models to practice scenarios.

7. Utilize programming skills to read, comprehend, and construct research concepts.

Textbooks

• Lecture Notes

Schedule