ENROLL

Mark McKone

An introduction to statistical techniques commonly used in Biology. The course will use examples from primary literature to examine the different ways that biological data are organized and analyzed. Emphasis will be placed on how to choose the appropriate statistical techniques in different circumstances and how to use statistical software to carry out tests. Topics covered include variable types (categorical, parametric, and non-parametric), analysis of variance, generalized linear models, and meta-analysis. There will be an opportunity for students to analyze data from their own research experiences.

Prerequisite: Biology 125 and 126 and one Biology 200 or 300 level course

Kathleen Galotti

An examination of the way people think and reason, both when given formal laboratory tasks and when facing problems and decisions in everyday life. Students consider their own reasoning and decision making through course exercises. Topics include models of formal reasoning, decision making, heuristics and biases in thinking and problem-solving, moral reasoning, improving skills of higher order cognition.

Prerequisite: Psychology 110 or Cognitive Science 100 or 130

Richard D. Wells

This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

Richard D. Wells

This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111.

Sophomore Priority

Waitlist for Juniors and Seniors: CS 111.WL2 (Synonym 64278)

Kiran Tomlinson

Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem"? The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs, and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201.

Prerequisite: Computer Science 111 or instructor permission

Sophomore Priority

Waitlist for Juniors and Seniors: CS 201.WL1 (Synonym 64282)

Kiran Tomlinson

Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem"? The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs, and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201.

Prerequisite: Computer Science 111 or instructor permission

Eric Alexander

Prerequisite: Computer Science 111 and Mathematics 111 or instructor permission

Jeff Ondich

Are you curious what's really going on when a computer runs your code? In this course we will demystify the machine and the tools that we use to program it. Our broad survey of how computer systems execute programs, store information, and communicate will focus on the hardware/software interface, including data representation, instruction set architecture, the C programming language, memory management, and the operating system process model.

Prerequisite: Computer Science 201 or instructor permission

David Musicant

What makes a programming language like "Python" or like "Java"? This course will look past superficial properties (like indentation) and into the soul of programming languages. We will explore a variety of topics in programming language construction and design: syntax and semantics, mechanisms for parameter passing, typing, scoping, and control structures. Students will expand their programming experience to include other programming paradigms, including functional languages like Scheme and ML.

Prerequisite: Computer Science 201 or instructor permission

Sneha Narayan

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)

Anna Rafferty

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)

Amy Csizmar Dalal

Prerequisite: Computer Science 201 or instructor permission

David Musicant

There are many situations where computer systems must make intelligent choices, from selecting actions in a game, to suggesting ways to distribute scarce resources for monitoring endangered species, to a search-and-rescue robot learning to interact with its environment. Artificial intelligence offers multiple frameworks for solving these problems. While popular media attention has often emphasized supervised machine learning, this course instead engages with a variety of other approaches in artificial intelligence, both established and cutting edge. These include intelligent search strategies, game playing approaches, constrained decision making, reinforcement learning from experience, and more. Coursework includes problem solving and programming.

Prerequisite: Computer Science 201. Additionally Computer Science 202 is strongly recommended.

Kent D Lee

As multi-core machines become more prevalent, different programming paradigms have emerged for harnessing extra processors for better performance. This course explores parallel computation for both shared memory and distributed parallel programming paradigms. In particular, we will explore how these paradigms affect the code we write, the libraries we use, and the advantages and disadvantages of each. Topics will include synchronization primitives across these models for parallel execution, debugging concurrent programs, fork/join parallelism, example parallel algorithms, computational complexity and performance considerations, computer architecture as it relates to parallel computation, and related theory topics.

Prerequisite: Computer Science 201

Jenna Conklin

Morgan Rood

Prerequisite: Linguistics 115

Jenna Conklin

Prerequisite: Linguistics 217

Deanna Haunsperger

An introduction to the central ideas of calculus with review and practice of those skills needed for the continued study of calculus. Problem solving strategies will be emphasized. In addition to regular MWF class time, students will be expected to attend two problem-solving sessions each week, one on Monday or Tuesday, and one on Wednesday or Thursday.  Details will be provided on the first day of class.

Prerequisite: Not open to students who have received credit for Mathematics 111.

Extra Time Tuesday and Thursday TA group meetings required

Kate Meyer

Prerequisite: Requires placement via the Calculus Placement Exam 1, see Mathematics web page. Not open to students who have received credit for Mathematics 101.

McCleary A Philbin

Inverse functions, integration by parts, improper integrals, modeling with differential equations, vectors, calculus of functions of two independent variables including directional derivatives and double integrals, Lagrange multipliers.

Prerequisite: Mathematics 101, 111, score of 4 or 5 on Calculus AB Exam or placement via a Carleton placement exam. Not open to students who have received credit for Mathematics 211 or have a score of 4 or 5 on the AP Calculus BC exam

Steve T Scheirer

Inverse functions, integration by parts, improper integrals, modeling with differential equations, vectors, calculus of functions of two independent variables including directional derivatives and double integrals, Lagrange multipliers.

Prerequisite: Mathematics 101, 111, score of 4 or 5 on Calculus AB Exam or placement via a Carleton placement exam. Not open to students who have received credit for Mathematics 211 or have a score of 4 or 5 on the AP Calculus BC exam

Steve T Scheirer

Inverse functions, integration by parts, improper integrals, modeling with differential equations, vectors, calculus of functions of two independent variables including directional derivatives and double integrals, Lagrange multipliers.

Prerequisite: Mathematics 101, 111, score of 4 or 5 on Calculus AB Exam or placement via a Carleton placement exam. Not open to students who have received credit for Mathematics 211 or have a score of 4 or 5 on the AP Calculus BC exam

Caroline Turnage-Butterbaugh

Vectors, curves, calculus of functions of three independent variables, including directional derivatives and triple integrals, cylindrical and spherical coordinates, line integrals, Green's theorem, sequences and series, power series, Taylor series.

Prerequisite: Mathematics 120. This course cannot be substituted for Mathematics 211

Rob Thompson

Vectors, curves, calculus of functions of three independent variables, including directional derivatives and triple integrals, cylindrical and spherical coordinates, line integrals, Green's theorem, sequences and series, power series, Taylor series.

Prerequisite: Mathematics 120. This course cannot be substituted for Mathematics 211

Deanna Haunsperger

Prerequisite: Score of 4 or 5 on the AP Calculus BC exam, or placement via Calculus Placement Exam #3

MurphyKate Montee

Linear algebra centers on the study of highly structured functions called linear transformations. Given the abundance of nonlinear functions in mathematics, it may come as a surprise that restricting to linear ones opens the door to a rich and powerful theory that finds applications throughout mathematics, statistics, computer science, and the natural and social sciences. Linear transformations are everywhere, once we know what to look for. They appear in calculus as the functions that are used to define lines and planes in Euclidean space. In fact, differentiation is also a linear transformation that takes one function to another. The course focuses on developing geometric intuition as well as computational matrix methods. Topics include kernel and image of a linear transformation, vector spaces, determinants, eigenvectors and eigenvalues. 

Prerequisite: Mathematics 120 or Mathematics 211

MurphyKate Montee

Linear algebra centers on the study of highly structured functions called linear transformations. Given the abundance of nonlinear functions in mathematics, it may come as a surprise that restricting to linear ones opens the door to a rich and powerful theory that finds applications throughout mathematics, statistics, computer science, and the natural and social sciences. Linear transformations are everywhere, once we know what to look for. They appear in calculus as the functions that are used to define lines and planes in Euclidean space. In fact, differentiation is also a linear transformation that takes one function to another. The course focuses on developing geometric intuition as well as computational matrix methods. Topics include kernel and image of a linear transformation, vector spaces, determinants, eigenvectors and eigenvalues. 

Prerequisite: Mathematics 120 or Mathematics 211

Caroline Turnage-Butterbaugh

Prerequisite: Mathematics 232 and either Mathematics 210 or Mathematics 211

Sophomore Priority

Waitlist for Juniors and Seniors: MATH 236.WL0 (Synonym 64923)

Laura Chihara

(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

Joseph Johnson

Prerequisite: Mathematics 232 or instructor permission

Sunrose Shrestha

An exploration of the behavior of non-linear dynamical systems. Topics include one and two-dimensional dynamics, Sarkovskii's Theorem, chaos, symbolic dynamics,and the Hénon Map.

Prerequisite: Mathematics 232 or instructor permission

Rob Thompson

An introduction to mathematical ideas from numerical approximation, scientific computing, and/or data analysis. Topics will be selected from numerical linear algebra, numerical analysis, and optimization. Theory, implementation, and application of computational methods will be emphasized.

Prerequisite: Mathematics 232

Not open to students who have already received credit for Mathematics 295 Numerical Analysis

Kate Meyer

Prerequisite: Mathematics 236 or instructor permission

Claudio Gomez-Gonzales

Prerequisite: Mathematics 342

This course can be repeated only by students who took MATH 352 22-23

Mitchell Campbell

Prerequisite: Psychology 110, or Psychology/Cognitive Science 232/233, or instructor consent; Concurrent registration in Psychology 201 required

PSYC 201 required.

Andy Poppick

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

Prerequisite: Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250

Sophomore priority

Waitlist for Juniors and Seniors: STAT 120.WL1 (Synonym 65257)

Deepak Bastola

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

Prerequisite: Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250

Andy Poppick

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

Prerequisite: Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250

Deepak Bastola

(Formerly MATH 215) Introduction to statistics and data analysis. Practical aspects of statistics, including extensive use of statistical software, interpretation and communication of results, will be emphasized. Topics include: exploratory data analysis, correlation and linear regression, design of experiments, basic probability, the normal distribution, randomization approach to inference, sampling distributions, estimation, hypothesis testing, and two-way tables. Students who have taken Mathematics 211 are encouraged to consider the more advanced Mathematics 240/Statistics 250 (formerly Mathematics 265 and 275) Probability/Statistical Inference sequence.

Prerequisite: Not open to students who have already received credit for Psychology 200/201, Sociology/Anthropology 239 or Statistics 250

Sophomore Priority

Waitlist for Juniors and Seniors: STAT 120.WL4 (Synonym 65258)

Adam Loy

(Formerly Mathematics 285) This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.

Prerequisite: Statistics 120 (formerly Mathematics 215), Statistics 230 (formerly Mathematics 245) or Statistics 250 (formerly Mathematics 275)

Claire Kelling

(Formerly Mathematics 245) 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: Statistics 120 (formerly Mathematics 215), Statistics 250 (formerly Mathematics 275), Psychology 200, or AP Statistics Exam score of 4 or 5.

Katie St. Clair

(Formerly Mathematics 275) 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 240 Probability (formerly Mathematics 265)

Andy Poppick

(Formerly MATH 280) 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: Statistics 230 and instructor permission

All interested students are encouraged to add to the waitlist and the instructor will reach out after registration. This course is repeatable, but if the instructor cannot admit every student on the waitlist, priority will be given first to Statistics majors who have not previously taken the course and then to other students who have not taken the course.

Laura Chihara

(Formerly MATH 315) Topics include linear mixed effects models for repeated measures, longitudinal or hierarchical data and generalized linear models (of which logistic and Poisson regression are special cases) including zero-inflated Poisson models. Depending on time, additional topics could include survival analysis, generalized additive models or models for spatial data.

Prerequisite: Statistics 230 and 250 (formerly Mathematics 245 and 275) or permission of the instructor

Formerly Mathematics 345