Probability spaces, random variables, expectation, independence, concentration bounds, conditional probability, repeated trials, error bounds. Turing machines and finite automata. Deterministic, nondeterministic and probabilistic models of computation. Languages, regular languages and regular expressions. Methods for classifying computational problems as decidable or undecidable.
This course may not be repeated for credit.
Prerequisite(s)
- 3 units from Computer Science 219, 233 or 235; and Computer Science 251 or with department consent Statistics 213 and either Mathematics 271 or 273; and 3 units from Mathematics 249, 265 or 275; and Philosophy 279 or 377.
Antirequisite(s)
- Credit for Computer Science 351 and Computer Science 313 will not be allowed.
Sections
This course will be offered next in
Fall 2023.