Courses Related to Digital and Computational Studies Fall 2014


FYS 336 – Nanotechnology Project: Manipulating Atoms

A hands-on introduction to the interdisciplinary field of nanotechnology-technology based on nanometer-scale structures. Students break into groups and become “specialists” to complete a class-wide collaborative nanotechnology project. Possible projects include designing and building a simplified scanned probe microscope, and fabricating and characterizing nanostructures. Students learn to identify and organize the tasks required of a long-term project. Clear and effective communication is emphasized as students work within and among groups, give brief talks, and write more formal papers. No previous experience is assumed, but the collaborative nature of the seminar requires the full and active participation of all participants.
Monday / Wednesday 1:10 – 2:30 Friday 1:04 – 4:00

FYS 438. Animats, Minds, and Mobots: Exploring Cognitive Science with Lego Robotics.

Philosophers have traditionally treated minds and bodies as somehow distinct, with the mind serving as the executive center, directing action by directing the activity of an otherwise passive body. Embodied Cognition is an alternative view within the field of cognitive science. The central claim of embodied cognition is that our bodies and minds have co-evolved as partners in cognition and behavior. The nature of intelligence and the structure of our minds is determined by the shapes of our bodies, by the ways different organisms have evolved to perceive and meaningfully engage with their environment. Minds and bodies are not distinct on this account, but rather our minds extend into our bodies. In this First Year Seminar we will use a series of autonomous robotics exercises developed for the Lego Mindstorms platform to explore this debate. Along the way we will also introduce students to the role robotics research has played in the development of artificial intelligence and cognitive science.

BIMA 255A – Mathematical Models in Biology

Mathematical models are increasingly important throughout the life sciences. This course provides an introduction to deterministic and statistical models in biology. Examples are chosen from a variety of biological and medical fields such as ecology, molecular evolution, and infectious disease. Computers are used extensively for modeling and for analyzing data. Prerequisite(s): MATH 205
Tuesday / Thursday 1:10 – 2:30

CHEM 301 – Quantum Chemistry

Major topics include quantum mechanics, atomic and molecular structure, and spectroscopy. Prerequisite(s): CHEM 108A or CH/ES 108B, MATH 106, and PHYS 107. Corequisite(s): PHYS 108 and MATH 205.
Monday / Wednesday / Friday 9:30 – 10:50

MATH 355B. Graph Algorithms.

How can we create a network of cables between houses with minimum cost? Under what circumstances can a mail delivery van traverse all the streets in the neighborhood it serves without repeating any street? Graph theory is the branch of mathematics that provides the framework to answer such questions. Topics may include definitions and properties of graphs and trees, Euler and Hamiltonian circuits, shortest paths, minimal spanning trees, network flows, and graph coloring. Some of the class meetings are devoted to learning to program in Maple. Students then write computer programs to provide solutions to questions such as the ones mentioned before. Prerequisite(s): MATH S21
Monday / Wednesday / Friday 9:30 – 10:50

MUS 237 – Computers, Music, and the Arts

A hands-on study of music making with computers, using the facilities of the Bates Computer Music Studio. Topics include digital synthesis, sampling, MIDI communications, simple programming, and the aesthetics of art made with computers. No computing experience is presumed, and the course is especially designed for students of the arts who wish to learn about new tools. Work produced in the course is performed in concert. Instructor permission required
Monday / Wednesday / Friday 11:00 – 11:55

PHIL 237 – Computational Modeling, Intelligence, and Intelligent Systems

Artificial intelligence is an interdisciplinary research field dedicated to the study of intelligence and intelligent systems. The field draws on materials from computer science, psychology, neuroscience, and philosophy. Its history is closely associated with the development of cognitive science. This course provides a historical introduction and overview of theories and methods within the field with a strong focus on the development of computational modeling and simulation as research methods. Course work include hands-on modeling and simulation exercises that enable students to explore the practical applications and limits of different models for intelligenct behavior. No prior programming experience is required. New course beginning Fall 2014.
Tuesday / Thursday 11:00 – 12:30

PSYC 357. Computational Neuroscience.

In this course, students apply techniques from engineering and computer science to address fundamental questions of brain function. Using real data sets as objects of study, students explore how the brain encodes and represents information on cellular scales, and also how computational approaches can be brought to bear on traditional neuroscience disciplines being revolutionized by data-driven paradigms. All assignments, and most class work emphasizes computer programming in Matlab (though no background is assumed or expected). Specific topics include spike statistics, reverse correlation and linear models of encoding, dimensionality reduction, cell assembly analysis, and computational genomics. Prerequisite(s): NS/PY 200 and PSYC 218 or any 200-level mathematics course. New course beginning Fall 2014.
Monday / Wednesday / Thursday / Friday 11:00 – 11:55