Understanding Our Algorithmic World In this course through lectures, labs, and discussions, we will examine the nature of computers and their role in our lives. We will use the lens of multimedia programming to learn basic computer programming and how computers represent and manipulate many common forms of data, such as text and images. We will also talk about the history of computers and learn how they interoperate to create the world we know today, and we will examine the societal impacts of technology on our lives, including implications for privacy, access to resources, and the increasing role of algorithms in shaping our world.

Introduction to Programming through Simulation In this course we will introduce computer programming through the exploration and writing of computer simulations with applications to ecological sciences and social sciences. We will use NetLogo as a software/programming tool for developing agent-based simulations. Students will learn basic programming constructs such as variables, conditionals, loops, procedures, and recursion as well as tools for experimenting with computer simulations.  No prior experience in programming is expected or required. (not open to students who have taken CSCI 0145 or higher)

Introduction to Computing In this course we will provide a broad introductory overview of the discipline of computer science, with no prerequisites or assumed prior knowledge of computers or programming. A significant component of the course is an introduction to algorithmic concepts and to programming using Python; programming assignments will explore algorithmic strategies such as selection, iteration, divide-and-conquer, and recursion, as well as introducing the Python programming language. Additional topics will include: the structure and organization of computers, the Internet and World Wide Web, abstraction as a means of managing complexity, social and ethical computing issues, and the question "What is computation?" 3 hr. lect./1 hr. lab

Intensive Introduction to Computing In this course we will provide an introduction to the field of computer science, geared towards students with some prior computer science or programming experience, or a background in quantitative problem-solving (e.g., advanced math coursework). Students will learn a variety of algorithmic strategies, including iterative and recursive approaches, and how to implement those strategies as Python programs. We will study computational techniques utilized in the natural sciences, social sciences and other disciplines. Additional topics will include large-scale data analysis and the ethical issues introduced by computing technologies.

Computing for the Sciences In this course we will provide an introduction to the field of computer science geared towards students interested in mathematics and the natural sciences. We will study problem-solving approaches and computational techniques utilized in a variety of domains including biology, chemistry, physics, and engineering. Students will learn how to program in Python and other languages, how to extract information from large data sets, and how to utilize a variety of tools employed in scientific computation. The course has no prerequisites and assumes no prior experience with programming or computer science.

Mathematical Foundations of Computing In this course we will provide an introduction to the mathematical foundations of computer science, with an emphasis on formal reasoning. Topics will include propositional and predicate logic, sets, functions, and relations; basic number theory; mathematical induction and other proof methods; combinatorics, probability, and recurrence relations; graph theory; and models of computation.

Data Structures In this course we will study the ideas and structures helpful in designing algorithms and writing programs for solving large, complex problems. The Java programming language and object-oriented paradigm are introduced in the context of important abstract data types (ADTs) such as stacks, queues, trees, and graphs. We will study efficient implementations of these ADTs, and learn classic algorithms to manipulate these structures for tasks such as sorting and searching. Prior programming experience is expected, but prior familiarity with the Java programming language is not assumed.

Computer Architecture A detailed study of the hardware and software that make up a computer system. Topics include assembly language programming, digital logic design, microarchitecture, pipelines, caches, and RISC vs. CISC. The goal of the course is teaching students how computers are built, how they work at the lowest level, and how this knowledge can be used to write better programs.

Theory of Computation This course explores the nature of computation and what it means to compute. We study important models of computation (finite automata, push-down automata, and Turing machines) and investigate their fundamental computational power. We examine various problems and try to determine the computational power needed to solve them. Topics include deterministic versus non-deterministic computation, and a theoretical basis for the study of NP-completeness.

Algorithms and Complexity This course focuses on the development of correct and efficient algorithmic solutions to computational problems, on the underlying data structures to support these algorithms, and on the social implications of algorithms. Topics include computational complexity, analysis of algorithms, proof of algorithm correctness, some advanced data structures, algorithmic techniques including greedy and dynamic programming, and the consequences of real-world applications of algorithms. The course complements the treatment of NP-completeness in CSCI 0301.

Artificial Intelligence Artificial Intelligence (AI) is the study of computational systems that exhibit rational behavior. Applications include strategic game playing, medical diagnosis, speech and handwriting recognition, Internet search, and robotics. Course topics include intelligent agent architectures, search, knowledge representation, logical reasoning, planning, reasoning under uncertainty, machine learning, and perception and action. We will also discuss the social implications of AI systems. This course fulfills the Responsible Computing requirement for the Computer Science major.

Software Development This course examines the process of developing larger-scale software systems. Laboratory assignments emphasize sound programming practices, tools that facilitate the development process, and teamwork.

Programming Languages A systematic approach to concepts and features of programming languages. The course focuses on four major programming paradigms: procedural, object-oriented, functional, and logic programming languages. Students will program in several languages representing the different paradigms. Topics include grammars, data types, control structures, run-time organization, procedure activation, parameter passing, higher-order functions, lambda expressions, and unification.

Systems Programming Students will become intimately acquainted with the low-level software services that applications often take for granted.  Through a broad, project-based survey of core system libraries and UNIX system calls, students will explore process management, memory management, linking and loading, threading, synchronization, filesystem operations, and inter-process communication (networking).  In each area, students will build software using these building blocks, gaining an understanding of the behavior and efficiency of the tools at their disposal. Students will also gain experience building larger, more complex systems upon which applications can be built.  This course is ideal for students who wish to understand and construct the software infrastructure upon which user-level software depends. (CSCI 0202) 3 hrs. lect

Object-Oriented Programming and GUI Application Development In this coding-intensive course students will deepen their understanding of data structures, algorithms, and object-oriented programming concepts through development of GUI (Graphical User Interface) applications.  After a brief introduction to C++ and our development environment, Qt, we will immerse ourselves in them through work on an array of application development projects.  Along the way, we will be introduced to a number of software development principles and build an understanding of fundamental object-oriented concepts in C++, including classes and inheritance, templates, pointers, constructors/destructors, and ownership. (CSCI 0202 or by waiver) 3 hrs lect./disc.

Bioinformatics Algorithms In this course we will explore and implement algorithmic solutions to modern biology questions. Students will be introduced to motivating biological questions—such as, “How do we compare DNA sequences?”—and then implement solutions to those problems using dynamic programming, graph, randomized, combinatorial and/or other algorithmic approaches. At the completion of the course students will be able to precisely define computational biology problems, design an algorithmic solution and implement that solution in software. No biology background is assumed, but students are expected to be able to implement sophisticated algorithms in Python. This course fulfills the Responsible Computing requirement for the Computer Science major.

Quantum Computing In this course we will explore how quantum mechanics can be applied to problems in communications, algorithms, detection, and cryptography. We will learn how features such as entanglement, superposition, and no-cloning can sometimes give quantum systems an advantage over standard “classical” computers. We will also discuss the current situation and challenges facing experimental quantum computers, as well as the limits of quantum computing. No previous experience with quantum mechanics is required. (MATH 0200 or CSCI 0200) 3 hrs lect./disc.

Spatial Agent-Based Modeling In this course students will learn efficient data structures and design techniques for spatially-explicit agent-based modeling using the NetLogo programming language. Agent-based modeling techniques will be applied to problems in the social and natural sciences (such as graph pandemic modeling and population dynamics), mathematics and computational sciences (such as graph algorithms), and agent-based games. We will also explore some advanced programming features of NetLogo. We also discuss ethical aspects of computer simulation and how agent-based modeling might or might not fit in a framework of responsible computing.Students will design and implement a significant term software project. This course fulfills the Responsible Computing requirement for the Computer Science major.  (CSCI 0201).

Computational Complexity We will investigate the computational power of various resources. Examples include determining whether a computer with limited time or limited space can solve more difficult problems, attempting to characterize creativity as a resource, and comparing probabilistic and quantum computation. We will learn why we do not yet have answers to many of the most fundamental questions in computational complexity (like whether P=NP), and we will think critically about the value of studying these topics. Students enrolled in the College Writing (CW) section of the course will explore these ideas through writing, in particular, in three contexts that are critical for theoretical computer science: the proof (expert audience), the review paper (non-expert computer science audience), and the popular science article (educated public audience). (CSCI 0301).

Advanced Operating Systems An operating system manages the complex resources of modern computers and provides an interface between the user and the hardware. In this course, we will explore the key concepts of operating systems, including process, memory, and storage management; synchronization and deadlock; protection and security; and distributed systems. (not open to students who have taken CSCI 0314) (CSCI 315)  3 hrs lect.

Parallel Computing Most modern computer architectures are parallel at multiple scales. In this course students will learn to develop programs that can efficiently use those parallel resources to improve performance and solve ever larger problems. Through a project-based survey students will be introduced to parallel hardware (multicore processors, clusters, GPUs), memory models (shared vs. non-shared), locality, synchronization, and different parallel programming models (threads, MapReduce, message-passing, SIMT, and more). Programming assignments will be implemented in multiple languages. (CSCI 0202) 3hrs. lect./lab

Geometric Modeling The ability to describe three-dimensional geometric objects is fundamental for computer-aided design,  scientific computing, and computer graphics. In this course we will investigate common methods for  building and manipulating digital representations of three-dimensional curves, surfaces, and solids using  polygonal and polyhedral meshes. This includes topics in mesh parameterization, adaptation, and registration, as well as surface reconstruction and deformation. We will also review common numerical methods used in scientific computing, including the finite element method, and study techniques for visualization, analysis, and design. Students will implement labs and projects using C++, within a framework provided by the instructor. (CSCI 0202 and MATH 0200) 3 hrs. sem

Computational Geometry Computational geometry involves the design and analysis of algorithms and data structures for the solution to problems of a geometric and computational nature. It is an old field, involving aspects of algorithms, geometry, combinatorics and probability, whose roots can be found in algorithmic questions posed by Euclid. Modern applications of computational geometry include geographic information systems, medical imaging, computer-aided design, and robotics. This course will explore some famous problems in computational geometry, including convex hulls, Voronoi diagrams, proximity problems, and triangulations.

Computer Networks Computer networks have had a profound impact on modern society. This course will investigate how computer networks are designed and how they work. Examples from the Internet as well as our own campus network will be discussed.

Compiler Design An introduction to the design and construction of compilers and translators. Topics include context-free grammars, lexical analysis, symbol tables, top-down and bottom-up parsing, parser generators, error recovery, run-time organization, declaration processing, type checking, code generation, and optimization. Through the course of the semester students will implement a complete compiler for a simple programming language.

Embedded Systems In this course we will learn about microcontrollers (compact single-chip integrated circuits at the core of embedded systems), including their architecture and how they interface with the outside world. In laboratory assignments, we will experiment with different families of microcontrollers, analyze various types of interfaces, and learn how to connect with external sensors and devices. While gaining hands-on familiarity with the different aspects of embedded systems, teams of students will engage in a semester-long project to design and build their own embedded system.

Network Science Many social, ecological, and technological systems are networked – their structure arises from connections between many individual components. Network science is the scientific study of connected systems using tools from mathematics, computer science, physics, and beyond. In this course, we will study measurements of centrality, hierarchy, and segregation in networks; random graph models; algorithms for network data mining; models of agents interacting on networks; and network data visualization. Students will write mathematical arguments; perform simulation experiments; implement analysis techniques; read contemporary research papers; navigate existing software packages for network analysis; and complete a substantial group project.

Machine Learning Machine learning algorithms detect patterns in data and use these patterns to make decisions. This course introduces the theory and practice of machine learning. Tasks considered may include classification, regression, clustering, dimensionality reduction, text embedding, and reinforcement learning. Applications may include predictive analytics, data visualization, pattern recognition, and strategic game-playing. We will also discuss the social implications of automated decision systems. This course fulfills the Responsible Computing requirement for the Computer Science major.

Image Processing In this course students will learn basic image processing concepts and explore practical cross-disciplinary case studies in teams.  They will use open-source tools to tackle real-world image and video analysis problems ranging from enhancing and denoising to tracking and extracting information from images and videos. Collaboratively, students will get to apply these tools to a problem of their choice in a semester-long project.

Drone Robotics In this course we will introduce the fundamental concepts of robotics, specifically focusing on drones using current research and applications. Topics will include drone control, flight planning, obstacle avoidance, and sensing. We will utilize tools from computer vision, image processing, and artificial intelligence, and we will complete experiments using small drones in compliance with college policy.

Natural Language Processing In this course we will explore computational models for processing natural (human) language. We will introduce statistical and algorithmic techniques that are used to classify, generate, and understand language at the syntactic and semantic levels. We will explore applications such as parsing, information extraction, language modeling, and sentiment analysis. Assignments will involve constructing and modifying systems and will incorporate a variety of large corpora. We will also discuss the ethical concerns associated with current methods for collecting and labeling large corpora, and how language technologies might reflect and reinforce social hierarchies. This course fulfills the Responsible Computing requirement for the Computer Science major.

Computer Graphics Computer graphics is the study of how computers represent, manipulate, and ultimately display visual information. In this course we will focus primarily on three-dimensional graphics, touching on topics such as modeling (meshes, hierarchical models, and transformations), rendering (lighting, texturing, rasterization, and clipping), animation, and GPU programming. We will look at the mathematical foundations of these techniques as well as implementation techniques using WebGL.

Information Visualization Information visualization is used to reveal patterns, trends, and outliers within abstract data. In this course we will cover topics such as the transformation of data to visual representations, common approaches to dealing with different types of data, perceptual issues that govern how visualizations are interpreted, and the development of interactive visualization tools. This course will culminate in a significant final visualization project.

Usable Interface Design for Mobile Applications In this course we will explore the fundamental concepts of human-computer interaction and interface design. We will focus on applying an iterative, human-centric design process to mobile development.  Topics will include user interface design, user experience, usability, prototyping, user testing, and mobile development. A significant portion of the class will be spent developing a mobile app, walking it through the various prototyping and testing stages.

Generative Art In this course we will explore the field of generative art – the artistic practice based on the creation of processes that yield art and design as an output. Through projects, we will find new applications for computing techniques such as visualization, physical simulation, stochastic processes, agent-based modeling, iterated function systems, fractals, genetic algorithms and machine learning. A portion of the class will also be devoted to reading research literature and discussing the nature of computation creativity. A background in art is not required.

Senior Seminar The senior seminar provides a capstone experience for computer science majors at Middlebury College. We will discuss strategies for conducting research and developing large-scale software while effectively communicating technical concepts through writing and presentations. In teams, students will identify a research question or software development project and then design, develop, test and analyze their final products. Lectures will be complemented by readings and discussions related to the impacts of computing on society. This course fulfills the Responsible Computing requirement for the Computer Science major.

Senior Thesis The senior thesis is required for all CSCI majors who wish to be considered for high and highest departmental honors, and is recommended for students interested in pursuing graduate study in computer science. Students will spend the semester researching and writing, and developing and experimenting as appropriate for their topic. All students will be expected to report on their work in the form of a written thesis, a poster, and an oral presentation at the end of the semester. In addition, throughout the semester, students will meet as a group to discuss research and writing, and will be expected to attend talks in the Computer Science lecture series. Before approval to join the class is granted, students are expected to have chosen a thesis adviser from the CSCI faculty, and determined a thesis topic with the guidance and approval of that adviser.

Crash Course in Systems Security In this course students will learn the theory and practice of computer systems security.  Morning lectures will be complemented by afternoon lab-sessions in which, under the close guidance of the instructor, students will complete both individual and group projects that will deepen their understanding of how (in)secure systems are implemented.  Students will learn to use industry-standard tools for performing analysis of system vulnerabilities; be introduced to the systems security research landscape; and gain an understanding of ethical, political, and financial issues surrounding systems security research.

Applied Data Science: Exploration, Prediction, and Generative Artificial Intelligence From predictive models to image and language generators, data science, machine learning, and generative artificial intelligence (AI) rely on computational methods for learning patterns in large, complex, datasets. In this hands-on course, we will apply multiple approaches for learning from data, including exploratory analysis, prediction, and generation. Students will describe social impacts of data-intensive technologies; manage and explore data sets; build predictive models; and programmatically apply generative models to real-world problems and data. This course is designed for students with introductory programming or data science preparation seeking to build foundational understanding and practical skills in machine learning and generative AI. This course fulfills the Responsible Computing requirement for the Computer Science major.

Deep Learning As data becomes ubiquitous and computing resources cheaper, many disciplines have turned to deep learning to solve complicated problems. While it has achieved remarkable success in a variety of "human" tasks, deep learning is often treated as a black-box. In this course, we will study deep learning from its foundations and build an intuitive understanding for why it works. Pairing lectures with labs, we will develop cutting-edge deep learning solutions to a variety of real-world problems. We will cover neural networks, convolutional networks designed for object detection, and recurrent networks used for natural language processing. We may also explore other topics including transformers, reinforcement learning, and generative adversarial networks subject to time and interest.

Randomized Algorithms for Data Science In this class, we will discover how data science techniques are deployed at scale. The questions we investigate will include: How do services such as Shazam recognize song clips in seconds? In settings with hundreds of features, how do we find patterns? Given a social network, how can we detect groups? And how can we use vibrations to "see" into the earth? We'll answer these questions and more by exploring how randomization lets us get away with far fewer resources than we'd otherwise need. Topics include random variables, concentration inequalities, dimensionality reduction, singular value decomposition, spectral graph theory, and approximate linear regression. (MATH 0200 or CSCI 0200 and CSCI 0302)  Teal Witter is a PhD candidate at NYU Tandon. He graduated from Middlebury in 2020 and can't wait to return to snowy Vermont for the winter term!/

Computer Vision In this course students will get a broad introduction to the field of computer vision, touching on topics ranging from low-level image processing to high-level geometric and semantic understanding of images. Along the way, we will draw on tools and techniques from mathematics, signal processing, and deep learning. The course is project-focused by design: interactive lectures will introduce theory, tools, and techniques, in support of several significant projects in which students will implement working vision systems for tasks such as image compositing, depth estimation, and novel view synthesis. We will also touch on recent advances in areas such as image generation, image and video segmentation, and unsupervised representation learning.

Amy Briggs

Ananya Das

Andrea Vaccari

Christopher Andrews

Daniel Scharstein

Frank Swenton

Jason Grant

John Foley

Laura Biester

Matthew Dickerson

Michael Linderman

Peter Johnson

Phil Chodrow

Philip Caplan

Scott Wehrwein

Shelby Kimmel

Shrutarshi Basu

Siegfried Bleher

Teal Witter

Read 763 reviews for Computer Science courses at Middlebury College. Find the best Computer Science professors and courses.  Discover your new major today!

I took this class because it was required for the CS major. The class itself covers very basic knowledge about how computers work, starting from basic circuitry and building up to machine code. The class homework is mostly putting class lectures into practice, and there are quizzes every week.

Wow, chodrow is absolutely amazing. I feel like the course was structured very well, in that you basically go from being a statistics course to really learning about ML (CNNs and other models), until you finally apply your knowledge. It is extremely logical and felt pertinent especially with the opportunity to try out some GPT experimentation too.

Prof. Dickerson is a nice professor, and the course material is of course in his expertise. The course covers a wide range of algorithms, and you get to implement each one through coding assignments. It was a valuable experience that helped me develop my algorithmic intuition and programming skills. However, I would say the course is a bit loosely structured, and there is room to cover more material.