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Grades: 484
Median GPA: A-
Mean GPA: 3.360
3.4
Professor rating
3.9
Difficulty
7
Ratings given
29%
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: 4,012
Median GPA: A-
Mean GPA: 3.223
Click a checkbox to add something to compare.
Grades: 484
Median GPA: A-
Mean GPA: 3.360
3.4
Professor rating
3.9
Difficulty
7
Ratings given
29%
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: 4,012
Median GPA: A-
Mean GPA: 3.223
Click a checkbox to add something to compare.
