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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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CS 4375 Anurag Nagar | |||||
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CS 4375 Anurag Nagar | |||||
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Grades: 5,196
Median GPA: A
Mean GPA: 3.729
4.4
Professor rating
3
Difficulty
67
Ratings given
89%
Would take again
Introduction to Machine Learning
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: 5,196
Median GPA: A
Mean GPA: 3.729
4.4
Professor rating
3
Difficulty
67
Ratings given
89%
Would take again
Introduction to Machine Learning
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
Click a checkbox to add something to compare.