ENROLL

Layla Oesper

How does Google always understand what it is you're looking for? How does Amazon.com figure out what items you might be interested in buying? How can categories of similar politicians be identified, based on their voting patterns? These questions can be answered via data mining, a field of study at the crossroads of artificial intelligence, database systems, and statistics. Data mining concerns itself with the goal of getting a computer to learn or discover patterns, especially those found within large datasets. We'll focus on techniques such as classification, clustering, association rules, web mining, collaborative filtering, and others.

Prerequisite: Computer Science 201, additionally, Computer Science 202 or Mathematics 236 strongly recommended

Sam Patterson

An introduction to ordinary differential equations, including techniques for finding solutions, conditions under which solutions exist, and some qualitative analysis.

Prerequisite: Mathematics 232 or instructor permission

Katie St. Clair

A second course in statistics covering simple linear regression, multiple regression and ANOVA, and logistic regression. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software to analyze real-life data.

Prerequisite: Mathematics 215 (or equivalent) or 275

Sophomore Priority

Waitlist for Juniors and Seniors: MATH 245.WL0 (Synonym 46271)

Sam Patterson

Algebra and geometry of complex numbers, analytic functions, complex integration, series, residues, applications. Not open to students who have already received credits for Mathematics 361.

Prerequisite: Mathematics 211

Andy Poppick

Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

Prerequisite: Mathematics 265

Katie St. Clair

Students will apply their statistical knowledge by analyzing data problems solicited from the Northfield community. Students will also learn basic consulting skills, including communication and ethics.

Prerequisite: Mathematics 245 and instructor permission

Waitlist Only. Instructor Permission required.

Liz K Sattler

Further topics in analysis such as measure theory, Lebesgue integration or Banach and Hilbert spaces.

Prerequisite: Mathematics 321 or instructor permission

Eric Egge

The study of structures involving finite sets. Counting techniques, including generating functions, recurrence relations, and the inclusion-exclusion principle; existence criteria, including Ramsey's theorem and the pigeonhole principle. Some combinatorial identities and bijective proofs. Other topics may include graph and/or network theory, Hall's ("marriage") theorem, partitions, and hypergeometric series.

Prerequisite: Mathematics 236 or instructor permission

Rob Thompson

Fourier series and their applications to boundary value problems in partial differential equations. Topics include separation of variables, orthogonal sets of functions, representations of functions in series of orthogonal functions, Sturm-Liouville theory, and Fourier transforms.

Prerequisite: Mathematics 241

Stephen Kennedy

Methods of teaching mathematics in grades 7-12. Issues in contemporary mathematics education. Regular visits to school classrooms and teaching a class are required.

Prerequisite: Junior or senior standing and instructor permission

Open only to Juniors and Seniors

Bob Dobrow

Introduction to the main discrete and continuous time stochastic processes. Topics include Markov chains, Poisson process, continuous time Markov chains, Brownian motion. Use of R and/or Mathematica.

Prerequisite: Mathematics 232 and 265