Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Algorithms
Learning evaluation functions for global optimization
Learning evaluation functions for global optimization
An introduction to the WEKA data mining system
Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education
Teaching AI through machine learning projects
Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education
Taking students out for a ride: using a board game to teach graph theory
Proceedings of the 38th SIGCSE technical symposium on Computer science education
Project MLEXAI: applying machine learning to web document classification
Journal of Computing Sciences in Colleges
MLeXAI: A Project-Based Application-Oriented Model
ACM Transactions on Computing Education (TOCE)
Educational advances in artificial intelligence
Proceedings of the 42nd ACM technical symposium on Computer science education
Hi-index | 0.00 |
Simple examples are teaching treasures. Finding a concise, effective illustration is like finding a precious gem. When such an example is fun and intriguing, it is educational gold. In this paper, we share the jeopardy dice game of Pig, which has extremely simple rules, engaging play, and a complex optimal policy. We describe its historical uses in mathematics, and share ways in which we have used the game to teach basic concepts in CS1, and intermediate concepts in introductory artificial intelligence, networking, and scientific visualization courses. We also describe the rich challenges Pig offers for undergraduate research in machine learning.