Course Descriptions

The Course Descriptions catalog describes all undergraduate and graduate courses offered by Michigan State University. The searches below only return course versions Fall 2000 and forward. Please refer to the Archived Course Descriptions for additional information.

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CSE 847 Machine Learning

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.
Effective Dates:
FS99 - US02

CSE 847 Machine Learning

Semester:
Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
CSE 841
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.
Effective Dates:
FS02 - US23

CSE 847 Machine Learning

Semester:
Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
CSE 840
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.
Effective Dates:
FS23 - Open