Computer Science encompasses both the theoretical and the practical aspects of the study of computers and algorithmic processes. Students majoring in computer science at Oberlin are prepared both for further graduate studies in the discipline and also for careers in the industries and businesses that actively recruit computer scientists with a liberal arts background. Computer Science at Oberlin is taught within the context of a liberal arts degree, with emphasis on the lasting principles of the discipline rather than on specific training in particular tools and techniques. The CS Department stresses the fundamentals of computer science while maintaining a highly current and relevant curriculum utilizing state-of-the-art methodologies and tools. More detailed information about the Computer Science major and minor and a complete list of courses can be found in the course catalog.
February 14th, 2013
November 26th, 2013
Come an learn new tricks for programming in Python including:
- list comprehensions
- higher-order functions (+ map, filter, reduce)
- sets and other useful data structures
- with statements/context managers
- the power of the python syntax
November 12th, 2013
On Thursday, November 14 there will be a talk by Dr. Matt Kretchmar of Denison University entitled Automated Identification of Text Message Authors: Was that really you who sent that text message?
Reception with light refreshments at 4:00pm in King 225, talk to follow at 4:30pm in King 239
Abstract: This talk is about the application of machine learning techniques to the problem of classifying authors of text messages. We use kernel-based support vector machines to build an automated classifier that uses statistical idiosyncrasies to distinguish one sender from others.
The talk is aimed at general undergraduate students in both mathematics and computer science.
October 30th, 2013
October 30th, 2013
Wednesday, November 6 NOON King 237
Thomas G. Dietterich of Oregon State University will present:
Opportunities for Machine Learning in Ecological Science and Ecosystem Management How can computer science address the many challenges of managing the earth's ecosystems sustainably? Viewed as a control problem, ecosystem management is challenging for two reasons. First, we lack good models of the function and structure of the earth's ecosystems. Second, it is difficult to compute optimal management policies because ecosystems exhibit complex spatio-temporal interactions at multiple scales. This talk will discuss some of the many challenges and opportunities for machine learning research in computational sustainability. These include sensor placement, data interpretation, model fitting, computing robust optimal policies, and finally executing those policies successfully. I'll provide examples from current work and discuss open problems in each of these areas. All of these sustainability problems involve spatial modeling and optimization, and all of them can be usefully conceived in terms of facilitating or preventing flows along edges in spatial networks. For example, encouraging the recovery of endangered species involves creating a network of suitable habitat and encouraging spread along the edges of the network. Conversely, preventing the spread of diseases, invasive species, and pollutants involves preventing flow along edges of networks. Addressing these problems will require advances in several areas of machine learning and optimization.
October 28th, 2013
Information Session for CSCI Majors Thursday, Oct. 31
12:15-1:15 King 327 Food and Drinks Provided
Have your questions answered by Staff and Faculty FROM AIT.
For more information about the program visit www.ait-budapest.com
October 1st, 2013
On Oct 5, 2013, 3 teams of Oberlin students competed at the 5th annual Muskingum University programming contest. Team “Albino Squirrels” (Jenny Ward ’14 and Nathan Klein ’16) took third place out of 15 teams from 6 institutions. Team “Uberlin” (Eli Stein ’14, Amanda Strominger ’15, Scott Hulver ’16) and “O(bees)” (Peter Fogg ’14, Oren Shoham ’14, Devon Wells ’14) also put in a strong showing.
September 30th, 2013
Monday Oct 7 Noon King 239
Disabling the MacBook Webcam Indicator LED
Disabling the MacBook Webcam Indicator LED Modern computers contain a surprising number of processors distinct from the CPUs, each dedicated to a specific task. These processors along with their perhipherals form embedded systems inside standard desktop and laptop systems which are frequently overlooked when evaluating the security of computer systems. In this talk, I’ll describe a security analysis of one such embeddedsystem: the Apple iSight webcam. The iSight contains, as a privacy feature, an indicator LED which provides a visual cue that the camera is turned on. I’ll describe how the hardware that controls the LED can be bypassed, enabling video to be captured without any indication to the user. I’ll also show how the iSight can be leveraged by malware to break out of a Virtual Machine sandbox.
Stephen Checkoway, is an Assistant Research Professor in the Johns
Hopkins University Department of Computer Science and a member of the
Johns Hopkins University Information Security Institute where he
teaches courses on computer security and software vulnerabilities. His
work includes security analyses of automotive emedded systems and
computer voting systems as well as offensive and defensive computer
security research. Checkoway earned bachelor’s degrees in mathematics
and computer sciences from the University of Washington in 2005 and a
Ph.D. in computer science in 2012 from the University of California,
RSVP in the CSCI office King 223 for Pizza lunch