My PlannerExplore and compare past grades, professor ratings, and reviews to find the perfect class.
Grades:
Median GPA:
Mean GPA:
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:
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:
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+ | ||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
MECH 6326 Justin Koeln | |||||
A | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
MECH 6326 Justin Koeln | |||||
A | |||||
Grades: 1,355
Median GPA: A-
Mean GPA: 3.435
Optimal Control and Dynamic Programming
MECH 6326
Erik Jonsson School of Engineering and Computer Science
Introduction to stochastic control, with applications taken from a variety of areas, including automatic control, engineering, supply-chain management, resource allocations, finance, etc. Markov chains and Markov decision processes, optimal policies and value functions with full state information for finite-horizon, infinite-horizon discounted, and average stage cost problems. Dynamic programming and Bellman equation, value iteration, policy iteration. Linear quadratic stochastic control. Approximate dynamic programming and model predictive control. 3 credit hours.
Prerequisite: MECH 6300 or instructor consent required.
Offering Frequency: Based on student interest and instructor availability
Grades: 82
Median GPA: A-
Mean GPA: 3.157
Click a checkbox to add something to compare.
Grades: 1,355
Median GPA: A-
Mean GPA: 3.435
Optimal Control and Dynamic Programming
MECH 6326
Erik Jonsson School of Engineering and Computer Science
Introduction to stochastic control, with applications taken from a variety of areas, including automatic control, engineering, supply-chain management, resource allocations, finance, etc. Markov chains and Markov decision processes, optimal policies and value functions with full state information for finite-horizon, infinite-horizon discounted, and average stage cost problems. Dynamic programming and Bellman equation, value iteration, policy iteration. Linear quadratic stochastic control. Approximate dynamic programming and model predictive control. 3 credit hours.
Prerequisite: MECH 6300 or instructor consent required.
Offering Frequency: Based on student interest and instructor availability
Grades: 82
Median GPA: A-
Mean GPA: 3.157
Click a checkbox to add something to compare.