Johns Hopkins University 3 credits Computer Science
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 β such as robustness to prediction error and generalization bounds β can be obtained. Required course background: 601.433/633 Algorithms and 601.475/675 Machine Learning or equivalent.
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