The departments with courses in the top 20 most enrolled this quarter include Civic, Liberal and Global Education (COLLEGE), computer science (CS), aeronautics and astronautics (AA) and math, among others.

The two most enrolled courses this fall, COLLEGE 101, a first-year course which discusses topics related to liberal arts education, and AA 228/CS 238, “Decision Making Under Uncertainty,” held the same spots last fall, with enrollment increasing by 133 and 50 students, respectively.

MATH 51, a course on multivariable calculus and linear algebra, which was the sixth most enrolled class last fall, moved up to become the third most enrolled this fall.

Six of the 10 most enrolled undergraduate classes are in CS, including introductory courses such as CS 106A: “Programming Methodology” and CS 106B: “Programming Abstractions.” CS 161: “Design & Analysis of Algorithms,” taught this fall by Associate Professor of Computer Science and Electrical Engineering Mary Wootters, saw roughly a 48 percent increase in enrollment. The course covers runtime analysis, data structures and algorithmic techniques such as divide-and-conquer and dynamic programming.

CHEM 31A: “Chemical Principles I” is among the most enrolled undergraduate courses this quarter. According to OnCourse, the class is historically composed largely of freshmen and sophomores. CHEM 31B: “Chemical Principles II” follows as its sequel and is part of the two-course general chemistry sequence that many pre-med students complete.

Among the top five most enrolled graduate courses are OB 205: “Managing Groups and Teams,” OB 206: “Organizational Behavior” and OB 209: “Leadership Laboratory.” All three are housed in the organizational behavior department within the Graduate School of Business and are required courses for first-year MBA students. These courses examine how individuals contribute to teams and how those teams grow and develop over time.

Following the OB courses, the sixth most enrolled graduate class is CS 229/STATS 229: “Machine Learning.” Offered in all three quarters, this four-unit course covers methods in supervised and unsupervised learning, along with topics in reinforcement learning. Prerequisites include CS 106B, CS 109 and MATH 51, or equivalent preparation.

The graphics below provide a breakdown of course enrollment within four schools: the Graduate School of Business, School of Education, Medical School and Law School.