Prerequisites: CMSC451 or equivalent; and an interest in probability (no major probability background is required, but general mathematical interest is necessary).

Must be in the Computer Science Master's or Doctoral programs, or permission of department

Concentration inequalities (large-deviation bounds) are of fundamental use in randomized algorithms, machine learning, data science, and other areas. This course will cover cutting-edge techniques in this field, and applications.