Applications of modern probability in data science, with an emphasis on randomization and the role of probabilistic techniques in computing; discrete random variables and expectation; deviation inequalities and applications to randomized algorithms; probabilistic methods and satisfiability; Monte Carlo method; sample complexity; combinatorial dimension. Prerequisites: MATH 304, MATH 309, MATH 311, or MATH 323; MATH 411 or STAT 414. Cross Listing: MATH 424/STAT 424 Credits 3. 3 Lecture Hours.
Applications of modern probability in data science, with an emphasis on randomization and the role of probabilistic techniques in computing; discrete random variables and expectation; deviation inequalities and applications to randomized algorithms; probabilistic methods and satisfiability; Monte Carlo method; sample complexity; combinatorial dimension. Prerequisites: MATH 304, MATH 309, MATH 311, or MATH 323; MATH 411 or STAT 414. Cross Listing: MATH 424/STAT 424 Credits 3. 3 Lecture Hours.
