Analyze recent developments on the role of machine learning in designing algorithms from the dual viewpoints of tailoring algorithm design to exploit the availability of data as well as tailoring learning to optimize for algorithm performance. Three hours a week for one semester Computer Science 388D and 395T (Topic: Data-Driven Algorithm Design) may not both be counted. Prerequisite: Graduate standing and knowledge of undergraduate algorithms and complexity, and at least one graduate course in theoretical computer science. Prior knowledge in randomized algorithms and machine learning.