Science and Mathematics

Computability and Logic — CS4383.01

Instructor: Darcy Otto

Credits: 4

This is not your typical class in computer science, or in formal logic; but you will learn a lot about both by taking it. Our subject will be one of the most important and influential papers that has ever been written—"On Computable Numbers, with an Application to the Entscheidungsproblem," by Alan Turing. This is the paper that birthed computer science as a discipline.

Science and Mathematics

Computational Linguistics — CS4122.01

Instructor: Justin Vasselli

Credits: 4

In this class, students will learn various techniques and algorithms for processing human languages. Topics we will cover include data structures and algorithms for text processing, tokenization, and part-of-speech tagging among other topics. Students will learn techniques for working with large amounts of data, and gain familiarity with common resources such as the Penn

Science and Mathematics

Computer Science Principles — CS2131.01

Instructor: Meltem Ballan

Credits: 4

This course is designed for all students. Computer Science Principles is an introductory course that introduces students to the breadth of the field of computer science. Students will learn to design and evaluate solutions and to apply computer science to solve problems through the development of algorithms and programs. Students will be provided real world insights,

CAPA Science and Mathematics

Computer Systems — CS4312.02

Instructor: acencini@bennington.edu

Credits: 4

A close look at how the unix operating system runs processes. Topics include machine-level data representation, C code and its compiled x86 assembly, virtual memory, process swapping, stack overflows, forking, the system heap, how compiling and linking are implemented, and inter-process communication. This material is a standard intermediate level part of undergrad computing

Science and Mathematics

Computing and Data in Practice — CS4389.01

Instructor: Michael Corey

Days & Time: Tu 8:30AM-10:20AM

Credits: 2

For students doing work-study or internships, we will focus on three core areas of professionalization. First, each week will journal our work weeks, discussing and sharing our work experiences in a round-table. Second, we will build our professionalization skills, especially networking (in person and on LinkedIn), resume writing, and

Computer Science

Computing and Data in Practice — CS4392.01

Instructor: Michael Corey

Days & Time: Tu 8:30AM-10:20AM

Credits: 2

For students doing work-study or internships, we will focus on three core areas of professionalization. First, each week will journal our work weeks, discussing and sharing our work experiences in a round-table. Second, we will build our professionalization skills, especially networking (in person and on LinkedIn), resume writing, and

Computer Science

Confidently Unsure: Interpreting Statistical Tests Wisely — MAT2248.01

Instructor: Andrew McIntyre

Credits: 2

No matter our focus, data and information are relied upon in making decisions, building hypotheses, or in trying to show the connection of one idea or thought to another. In order to better understand (or argue against) a claim, we need to make sure we understand what the data is telling us and how it can be interpreted. This course will build towards understanding the basic

Science and Mathematics

Conjecture and Proof in Discrete Mathematics — MAT4131.01

Instructor: Steven Morics

Credits: 4

Using concepts from combinatorial mathematics and computer science, this course is an introduction to the nature and process of doing mathematics; playing around with patterns, making conjectures, and then stating and proving theorems. The course revolves around a large collection of open-ended problems, concerning topics from graph theory, game theory, set theory,

Science and Mathematics

Conservation Biology — BIO2129.01

Instructor: Carly Rudzinski

Credits: 4

This course introduces the unifying concepts of the diverse and interdisciplinary field of conservation biology, as well as highlighting the history of conservation in practice and current issues and methods. We will discuss conservation issues that span and integrate across disciplines and levels of organization, including: biodiversity and

Science and Mathematics

Conservation Biology (with Lab) — BIO4133.01

Instructor: Carly Rudzinski

Credits: 4

This course introduces the unifying concepts of the diverse and interdisciplinary field of conservation biology, as well as highlighting the history of conservation in practice and current issues and methods. We will discuss conservation issues that span and integrate across disciplines and levels of organization, including: biodiversity and ecological functions,

Science and Mathematics

Conservation Paleobiology — BIO4190.01

Instructor: Carly Rudzinski

Credits: 2

Most conservation biology studies are fairly short-term: years to decades. But, many of the threats to biodiversity, including environmental change, unfold over longer timelines, and dynamic ecological responses to disturbances may not be fully captured in short studies. Paleobiology — the study of fossil organisms — can extend our understanding of population and community

Science and Mathematics

Creation of Statistics — MAT2247.01

Instructor: Andrew McIntyre

Credits: 4

The amount of data in the world is vast and is increasing exponentially. It is easy to become overwhelmed and lose sight of the goal of data: to answer questions we have about the world in a specific, concise manner. The goal of this course is to help craft answerable questions—and then answer them. In order to do this, we will be using a programming language (“R”) to help us

Science and Mathematics

Creation of Statistics — MAT2247.01

Instructor: Josef Mundt

Credits: 4

The amount of data in the world is vast and is increasing exponentially. It is easy to become overwhelmed and lose sight of the goal of data: to answer questions we have about the world in a specific, concise manner. The goal of this course is to help craft answerable questions—and then answer them. In order to do this, we will be using a programming language (“R”) to help us

Science and Mathematics

Creation of Statistics — MAT2247.01

Instructor: Josef Mundt

Credits: 4

The amount of data in the world is vast and is increasing exponentially. It is easy to become overwhelmed and lose sight of the goal of data: to answer questions we have about the world in a specific, concise manner. The goal of this course is to help craft answerable questions---and then answer them. In order to do this, we will be using a programming language ("R") to help us

Environment Science and Mathematics Society Culture and Thought

Credibility in Social Media: Fake News and Fact Checking — CS2234.01

Instructor: Ursula Wolz

Credits: 2

How do you know what is credible on social media news feeds? This seven week course introduces the emerging field of Algorithm Accountability as it is applied to natural language processing and interactive journalism systems. Using application programmer interfaces, students, regardless of prior programming knowledge, will develop the expertise to experiment with (1)

Science and Mathematics

Darwin and the Naturalists — BIO4223.01

Instructor: Kerry Woods

Credits: 2

Much of modern biology is rooted in insights of a series of 18th and 19th-century naturalist-scientist-explorers who built upon extensive and inspired observation, sometimes in the course of travels in (then) remote and challenging parts of the world. Their writings often took the form of journals interlarded with theoretical speculation, and some achieved great popularity

Science and Mathematics Environment

Darwin and the Naturalists — BIO4223.01

Instructor: Kerry Woods

Credits: 2

Much of modern biology is rooted in the insights of a series of 18th and 19th-century naturalist-scientist-explorers who built upon extensive and inspired observation, sometimes in the course of travels in (then) remote and challenging parts of the world. Their writings often took the form of journals interlarded with theoretical speculation, and achieved great popularity

Science and Mathematics Environment

Data Structures and Algorithms — CS4388.01

Instructor: Darcy Otto

Days & Time: TU,FR 2:10pm-4:00pm

Credits: 4

How do we organize data to solve complex problems efficiently? This course studies the fundamental structures and algorithms that form the cornerstone of computational problem-solving. Building upon the programming foundations established in CS1, we will explore how algorithmic thinking and sophisticated data organization enables us to tackle increasingly challenging

Computer Science

Data Visualization and Data Structures — CS2235.01

Instructor: Ursula Wolz

Credits: 4

Data in a computer is simply patterns of bits, often represented as ‘1’s and ‘0’s. But what that data represents ranges from complex text (poetry, dialog, exposition, debate) to rich graphics in 2 or 3 dimensions, either still or animated, and increasingly as physical sculpture, robot choreography, mixed media, and augmented reality. Data visualization is the study of how to

Science and Mathematics

Database Management Systems — CS4311.01

Instructor: Andrew Cencini

Credits: 4

In the age of “Big Data”, the problem of storing, managing and gaining insight from data is more pressing than ever. Additionally, the world of data management has exploded, with more products and services on offer than ever before. In this class, we will explore the problem of storing, managing and querying structured, semi-structured, and unstructured data by learning how

Science and Mathematics

Design Patterns and Data Structures — CS4106.01

Instructor: Justin Vasselli

Credits: 4

In this class, students will learn common patterns used to solve problems found in software, and gain a deeper knowledge about common ways that data is stored and accessed. Students will learn about the design and implementation of data structures, including inked lists, stacks, queues, and trees. Students will also study common algorithms used to populate and query these data

Science and Mathematics

Design Patterns and Data Structures — CS4106.01

Instructor: Justin Vasselli

Credits: 4

In this class, students will learn common patterns used to solve problems found in software, and gain a deeper knowledge about common ways that data is stored and accessed. Students will learn about the design and implementation of data structures, including arrays, linked lists, stacks, queues, and trees. Students will also study common algorithms used to populate and query

Science and Mathematics

Differential Equations and Dynamical Systems — MAT4108.01

Instructor: Kathryn Montovan

Credits: 4

Differential equations are the most powerful and most pervasive mathematical tool in the sciences and are fundamental in pure mathematics as well. Almost every system whose components interact continuously over time can be modeled by a differential equation, for example, planets, stars, fluids, electric circuits, predator and prey populations, epidemics, and economics. We will

Environment Science and Mathematics

Differential Equations and Non-linear Dynamical Systems — MAT4108.01

Instructor: Katie Montovan

Credits: 4

Differential equations are a powerful and pervasive mathematical tool in the sciences and are fundamental in pure mathematics as well. Almost every system whose components interact continuously over time can be modeled by a differential equation, and differential equation models and analyses of these systems are common in the literature in many fields including physics, ecology

Science and Mathematics

Differential Geometry, Gauge Theories, and Gravity — MAT4302.01

Instructor: Andrew McIntyre

Credits: 4

The concept of a curved space is something that mathematicians developed for their own internal, logical reasons throughout the nineteenth and early twentieth centuries. In the twentieth century, it has become apparent that these theories are deeply interwoven with our understanding of nature, from Einstein's description of gravity as the curvature of spacetime, through

Science and Mathematics