Machine learning in the liberal arts curriculum

Authors:
Clare Bates Congdon
Affiliations:
Computer Science Department, Colby College, 5846 Mayflower Hill Drive, Waterville, ME
Venue:
Proceedings of the thirty-first SIGCSE technical symposium on Computer science education
Year:
2000

Citing 7
Cited 0

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Elements of machine learning

Elements of machine learning
An introduction to genetic algorithms

An introduction to genetic algorithms
Machine Learning

Machine Learning
Chunking in Soar: The Anatomy of a General Learning Mechanism

Machine Learning
Induction of Decision Trees

Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Machine learning is typically considered a graduate-level course with an artificial intelligence course as a prerequisite. However, it does not need to be positioned this way, and in the liberal arts curriculum in particular, there are advantages to offering this course to undergraduate students. An undergraduate course in machine learning is easily and naturally structured to introduce research concepts and to work within a research paradigm. It also introduces the use of statistics, reflected both in the machine learning systems studied and in the experimental methodology. Furthermore, it allows for an interdisciplinary perspective, as students can be encouraged to work on problems from other departments in the college. This paper describes the benefits of offering such a course and outlines a course structure and resources for doing so.