Grades:
Median GPA:
Mean GPA:
5.0
Professor rating
5.0
Difficulty
1,000
Ratings given
99%
Would take again
Visit Rate My Professors
Grades:
Median GPA:
Mean GPA:
Click a checkbox to add something to compare.
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Search Results
| Name | Grades | Rating |
|---|---|---|
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
A+ | ||
Grades: 541
Median GPA: A-
Mean GPA: 3.450
3.6
Professor rating
4.2
Difficulty
16
Ratings given
71%
Would take again
Intelligent Systems Design
CS 4315 (Same as CGS 4315)
Erik Jonsson School of Engineering and Computer Science
This advanced machine learning course covers mathematics essential for the analysis and design of unsupervised, supervised, and reinforcement machine learning algorithms including deep learning neural network models formulated within a statistical empirical risk minimization framework. Topics include: convergence analysis of adaptive and batch learning algorithms, Monte Carlo Markov Chain inference algorithms, bootstrap sampling methods, and the statistical analysis of generalization performance using model selection measures such as AIC and BIC. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisite: CGS 4314 or CS 4314.
Offering Frequency: Every two years
Grades: 11
Median GPA: A-
Mean GPA: 3.303
Click a checkbox to add something to compare.
Grades: 541
Median GPA: A-
Mean GPA: 3.450
3.6
Professor rating
4.2
Difficulty
16
Ratings given
71%
Would take again
Intelligent Systems Design
CS 4315 (Same as CGS 4315)
Erik Jonsson School of Engineering and Computer Science
This advanced machine learning course covers mathematics essential for the analysis and design of unsupervised, supervised, and reinforcement machine learning algorithms including deep learning neural network models formulated within a statistical empirical risk minimization framework. Topics include: convergence analysis of adaptive and batch learning algorithms, Monte Carlo Markov Chain inference algorithms, bootstrap sampling methods, and the statistical analysis of generalization performance using model selection measures such as AIC and BIC. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisite: CGS 4314 or CS 4314.
Offering Frequency: Every two years
Grades: 11
Median GPA: A-
Mean GPA: 3.303
Click a checkbox to add something to compare.
