Description:
Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
Algorithms, programming in C or equivalent, probability and statistics, artificial intelligence.
Restrictions:
Open only to students in the Department of Computer Science and Engineering or approval of department.
Description:
Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
Algorithms, programming in C or equivalent, probability and statistics, artificial intelligence.
Restrictions:
Open to graduate students in the Department of Computer Science and Engineering or approval of department.
Description:
Computational study of learning and data mining. Strengths and limitations of various learning paradigms, including supervised learning, learning from scalar reward, unsupervised learning, and learning with domain knowledge.