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CS 6347 Nicholas Ruozzi | |||||
A | |||||
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CS 6347 Nicholas Ruozzi | |||||
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Grades: 642
Median GPA: B+
Mean GPA: 3.198
3.6
Professor rating
4.8
Difficulty
32
Ratings given
50%
Would take again
Statistical Methods in AI and Machine Learning
CS 6347
Erik Jonsson School of Engineering and Computer Science
Introduction to the probabilistic and statistical techniques used in modern computer systems. Probabilistic graphical models such as Bayesian and Markov networks. Probabilistic inference techniques including variable elimination, belief propagation and its generalizations, and sampling-based algorithms such as importance sampling and Markov Chain Monte Carlo sampling. Statistical learning techniques for learning the structure and parameters of graphical models. Sequential models such as Hidden Markov models and Dynamic Bayesian networks. 3 credit hours.
Prerequisites: (CS 3341 or equivalent) and (CS 5343 or equivalent).
Offering Frequency: Each year
Grades: 302
Median GPA: A
Mean GPA: 3.623
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Grades: 642
Median GPA: B+
Mean GPA: 3.198
3.6
Professor rating
4.8
Difficulty
32
Ratings given
50%
Would take again
Statistical Methods in AI and Machine Learning
CS 6347
Erik Jonsson School of Engineering and Computer Science
Introduction to the probabilistic and statistical techniques used in modern computer systems. Probabilistic graphical models such as Bayesian and Markov networks. Probabilistic inference techniques including variable elimination, belief propagation and its generalizations, and sampling-based algorithms such as importance sampling and Markov Chain Monte Carlo sampling. Statistical learning techniques for learning the structure and parameters of graphical models. Sequential models such as Hidden Markov models and Dynamic Bayesian networks. 3 credit hours.
Prerequisites: (CS 3341 or equivalent) and (CS 5343 or equivalent).
Offering Frequency: Each year
Grades: 302
Median GPA: A
Mean GPA: 3.623
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