ENROLL

Jeffrey Ondich

A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis.

Prerequisite: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

Josh Davis

An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finite-state automata, pushdown automata, and Turing machines; formal languages, including regular expressions and context-free grammars; computability and uncomputability; and computational complexity, particularly NP-completeness.

Prerequisite: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

Katie St. Clair

(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

Prerequisite: Mathematics 120 or Mathematics 211

Formerly Mathematics 265

Katie St. Clair

(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

Prerequisite: Mathematics 120 or Mathematics 211

Formerly Mathematics 265

Kate Meyer

Prerequisite: Mathematics 232 or instructor permission

Caroline Turnage-Butterbaugh

A first course in number number theory, covering properties of the integers. Topics include the Euclidean algorithm, prime factorization, Diophantine equations, congruences, divisibility, Euler’s phi function and other multiplicative functions, primitive roots, and quadratic reciprocity. Along the way we will encounter and explore several famous unsolved problems in number theory. If time permits, we may discuss further topics, including integers as sums of squares, continued fractions, distribution of primes, Mersenne primes, the RSA cryptosystem.

Prerequisite: Mathematics 236 or instructor permission

Formerly Math 312

Claudio Gomez-Gonzales

Prerequisite: Mathematics 236 or instructor permission

Rob Thompson

Prerequisite: Mathematics 236 or permission of the instructor.

MurphyKate Montee

Geometric group theory is the study of (infinite) groups using geometric tools. The underlying principle of geometric group theory is that if a group G acts "nicely" on a space, then information about that space tells us information about the group. This class will introduce tools from topology, graph theory, and geometry and use them to study groups. Topics will include groups acting on trees and (more generally) hyperbolic groups. This course counts toward the Algebra area of the math major.

Prerequisite: Mathematics 342 or instructor consent

Adam Loy

Formerly MATH 315) An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

Prerequisite: Statistics 250 (formerly Mathematics 275)

Fomerly Mathematics 315