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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Course Descriptions: Search Results

CMSE 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major.
Description:
Core principles and techniques of all machine learning including model design and programming algorithms.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS19 - US21

CMSE 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and MTH 314
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Data Science Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS21 - US25

CMSE 404 Introduction to Machine Learning

Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and (MTH 314 or MTH 317H)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Computer Science Minor or in the Data Science Major or in the Lyman Briggs Data Science Coordinate Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS25 - Open

CSE 404 Introduction to Machine Learning

Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and (MTH 314 or MTH 317H)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Computer Science Minor or in the Data Science Major or in the Lyman Briggs Data Science Coordinate Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS25 - Open

CSE 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and MTH 314
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Data Science Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS21 - US25

CSE 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major.
Description:
Core principles and techniques of all machine learning including model design and programming algorithms.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS19 - US21

STT 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major.
Description:
Core principles and techniques of all machine learning including model design and programming algorithms.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Computer Science and Engineering
Administered By:
Computer Science and Engineering
Effective Dates:
FS19 - US21

STT 404 Introduction to Machine Learning

Semester:
Fall of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and MTH 314
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Data Science Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Computer Science and Engineering
Administered By:
Computer Science and Engineering
Effective Dates:
FS21 - US25

STT 404 Introduction to Machine Learning

Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and (MTH 314 or MTH 317H)
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Computer Science Minor or in the Data Science Major or in the Lyman Briggs Data Science Coordinate Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Computer Science and Engineering
Administered By:
Computer Science and Engineering
Effective Dates:
FS25 - Open