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5.0
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5.0
Difficulty
1,000
Ratings given
99%
Would take again
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Search Results
| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 4375 Latifur Khan | |||||
A- | |||||
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 4375 Latifur Khan | |||||
A- | |||||
Grades: 1,327
Median GPA: A-
Mean GPA: 3.619
3.4
Professor rating
2.8
Difficulty
25
Ratings given
77%
Would take again
Introduction to Machine Learning
CS 4375
Erik Jonsson School of Engineering and Computer Science
Algorithms for creating computer programs that can improve their performance through learning. Topics include: cross-validation, decision trees, neural nets, statistical tests, Bayesian learning, computational learning theory, instance-based learning, reinforcement learning, bagging, boosting, support vector machines, Hidden Markov Models, clustering, and semi-supervised and unsupervised learning techniques. 3 credit hours.
Prerequisites: (CS 3341 or SE 3341 or (Data Science major and STAT 3355)) and (CE 3345 or CS 3345 or SE 3345) or equivalent.
Offering Frequency: Each year
Grades: 4,442
Median GPA: B+
Mean GPA: 3.234
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Grades: 1,327
Median GPA: A-
Mean GPA: 3.619
3.4
Professor rating
2.8
Difficulty
25
Ratings given
77%
Would take again
Introduction to Machine Learning
CS 4375
Erik Jonsson School of Engineering and Computer Science
Algorithms for creating computer programs that can improve their performance through learning. Topics include: cross-validation, decision trees, neural nets, statistical tests, Bayesian learning, computational learning theory, instance-based learning, reinforcement learning, bagging, boosting, support vector machines, Hidden Markov Models, clustering, and semi-supervised and unsupervised learning techniques. 3 credit hours.
Prerequisites: (CS 3341 or SE 3341 or (Data Science major and STAT 3355)) and (CE 3345 or CS 3345 or SE 3345) or equivalent.
Offering Frequency: Each year
Grades: 4,442
Median GPA: B+
Mean GPA: 3.234
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