|Closed captioning will be available in English and Japanese for all keynotes and RSAC track sessions.
Please note: All times are in SGT.
AI and machine learning have been advancing at a rapid pace. Prerequisite knowledge in differential calculus, linear algebra, matrix math, statistics probability and complex mathematical structures block most security professionals from exploring this area. Let’s break down these barriers and help you understand how security vulnerabilities manifest themselves in AI and ML applications.
1: Understand what causes a neural network to be vulnerable.
2: Understand the techniques that can be used to trojan your network, steal your model or expose training data.
3: Learn about possible solutions against them.