Machine Learning Biases

This lecture covers the basics of machine learning, focusing on supervised learning and the challenges posed by adversarial conditions. It delves into adversarial examples, their creation, and the evolving landscape of attacks and defenses. The instructor discusses the impact of biases in machine learning models, the concept of distributional shift, and the implications of errors and fairness in classification tasks. Additionally, the lecture explores the complexities of deploying machine learning systems in real-world scenarios, highlighting issues such as base rate fallacy, externalized risks, and lack of transparency.