3.0

Average Course Rating

Artificial intelligence and machine learning hold significant promise for improving algorithmic decision-making across domains. This course will survey recent advances in integrating AI/ML models into algorithm design. We will focus on two different paradigms: algorithms with predictions, which seek to leverage black-box, potentially unreliable predictions to improve performance while maintaining robustness; and learning-based approaches, where AI/ML models are trained to directly perform algorithmic reasoning. Throughout, we will emphasize settings where provable guarantees – such as robustness to prediction error and generalization bounds – can be obtained. Required course background: 601.433/633 Algorithms and 601.475/675 Machine Learning or equivalent.