Explore alternatives to traditional worst-case analysis and their applications in various computational domains, including online algorithms, machine learning, and more.
This course has two intertwined goals. The first goal is to survey several important computational problems for which the traditional worst-case analysis of algorithms is ill-suited, because it fails to differentiate meaningfully between different solutions, recommends an empirically "wrong" solution over the "right" one, or gives excessively pessimistic performance predictions. The second goal is to study systematically alternatives to worst-case analysis that nevertheless enable rigorous and robust guarantees on algorithm performance.
This course is recommended for students with a background in undergraduate algorithms (CS161 or equivalent) who are interested in exploring alternatives to traditional worst-case analysis and their applications in various computational domains.
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