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17 courses found.
ELECTRICAL AND SYSTEMS ENGINEERING (E35)  (Dept. Info)Engineering and Applied Science  (Policies)SP2025

E35 ESE 500Independent StudyVar. Units (max = 3.0)
SecDays       TimeBuilding / RoomInstructorFinal ExamSeatsEnrollWaits
01TBA(None) / ClarkNo Final000
02TBA(None) / FeherDefault - none020
03TBATBAChingDefault - none000
04TBATBAJhaDefault - none000
07TBATBALewDefault - none000
08TBATBAChakrabarttyDefault - none010
09TBATBAWang, DorothyDefault - none000
11TBATBANaguluDefault - none000
13TBATBAO'SullivanDefault - none000
28TBATBAYangDefault - none000
29TBATBALiDefault - none001
30TBATBAShenDefault - none000
32TBATBAKurenokDefault - none001
34TBATBAMellDefault - none000
36TBATBAKamilovDefault - none000
37TBATBAZengDefault - none000
38TBATBAZhouDefault - none000
39TBATBAVillaDefault - none000
40TBATBAZhouDefault - none000
41TBATBAMurchDefault - none000
42TBATBASinopoliDefault - none000
43TBATBALuDefault - none000
44TBATBAChenDefault - none000

E35 ESE 527Practicum in Data Analytics & Statistics3.0 Units

E35 ESE 5581Advanced Systems Science for Learning and Control of Complex Dynamic Systems3.0 Units
Description:A rigorous introduction to recent trend and developments in systems and controls. Focuses are on the discussion of multidisciplinary applications of complex dynamical systems that motivate emerging topics in dynamics and control, data science, and learning, and on the review of essential tools and state-of-the-art methods for addressing control, computation, and learning for these large-scale systems. Topics to be covered include stochastic control, geometric control, and systems-enabled data science approaches to control and learning of high-dimensional systems. Technical tools to be discussed include basics of stochastic calculus, stochastic differential equations, and stochastic control; basics and applied differential geometry; principles of optimal control and dynamic programming, as well as reinforcement learning. Applications of these classical and advanced methods to complex networks, multi-agent systems, ensemble systems, neural networks, and manifold learning will be discussed. Students will be exposed to the state-of-the-art research in the field, and are expected to apply the learned knowledge to conduct a final project. Prerequisite: Linear algebra (Math 429) or equivalent, ESE 415 Optimization, ESE 551 Linear Dynamic Systems, and ESE 520 Probability and Stochastic Processes. Students without completing the listed prerequisite coursework may consult with the instructor for permission to enroll in this course.
Attributes:
Instruction Type:Classroom instruction Grade Options:CPA Fees:
Course Type:HomeSame As:N/AFrequency:None / History

E35 ESE 599Masters ResearchVar. Units (max = 3.0)
Description:Prerequisite: Students must have the ESE Research Registration Form. approved by the department. The form must contain a brief description of the work that is expected to be completed during the course.
Attributes:
Instruction Type:Dissertation/Research Grade Options:C Fees:
Course Type:HomeSame As:N/AFrequency:None / History
SecDays       TimeBuilding / RoomInstructorFinal ExamSeatsEnrollWaits
01TBATBAWormleightonNo Final001
02TBATBASorrellsNo Final010
03TBATBAChingDefault - none000
04TBATBAPatwariDefault - none000
07TBATBALewDefault - none000
08TBATBAChakrabarttyDefault - none001
09TBATBAWang, DorothyDefault - none000
10TBATBALawrenceDefault - none000
11TBATBAFeherDefault - none000
13TBATBAO'SullivanDefault - none000
22TBATBANaguluDefault - none000
28TBATBAYangDefault - none000
29TBATBALiDefault - none011
30TBATBAShenDefault - none000
32TBATBAKurenokDefault - none000
34TBATBAMellDefault - none000
36TBATBAKamilovDefault - none002
37TBATBAZengDefault - none000
38TBATBAZhouDefault - none000
39TBATBAVillaDefault - none000
40TBATBAClarkDefault - none001
41TBATBASotirasDefault - none000
42TBATBAZhuDefault - none000
43TBATBACulverDefault - none000
44TBA(None) / KantarosDefault - none000
45TBATBAChamberlainDefault - none001
46TBATBAMurchDefault - none000
47TBATBAWangDefault - none001
48TBA(None) / WangDefault - none000
49TBA(None) / ChenDefault - none010

E35 ESE 600Doctoral ResearchVar. Units (max = 9.0)
SecDays       TimeBuilding / RoomInstructorFinal ExamSeatsEnrollWaits
01TBA(None) / LawrenceDefault - none1010
02TBATBASorrellsDefault - none910
Actions:Books
03TBATBAChingDefault - none900
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04TBATBAZhouDefault - none910
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06TBATBAZhang, SilviaDefault - none900
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07TBATBALewDefault - none910
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08TBATBAChakrabarttyDefault - none900
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09TBATBAGoodhillDefault - none900
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10TBATBAMinDefault - none900
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11TBATBAWang, YongDefault - none910
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13TBATBAO'SullivanDefault - none900
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17TBATBASchaettlerDefault - none900
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18TBATBAShraunerDefault - none900
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27TBATBANehoraiDefault - none900
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28TBATBAYangDefault - none930
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29TBATBALiDefault - none1010
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30TBATBAShenDefault - none1000
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32TBATBAKurenokDefault - none1000
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34TBATBAMellDefault - none1000
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36TBATBAKamilovDefault - none900
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37TBATBAAnastasioDefault - none900
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38TBATBAZengDefault - none900
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39TBATBAPatwariDefault - none900
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40TBATBASotirasDefault - none99920
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41TBATBAJhaDefault - none99900
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42TBATBARamanDefault - none99900
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43TBATBATaiDefault - none99910
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44TBATBAZhuDefault - none99900
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45TBATBAWangDefault - none99900
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46TBATBASinopoliDefault - none99900
47TBATBAMonosovDefault - none99910
48TBATBAEggebrechtDefault - none99910
Actions:Books
49TBATBAKantarosDefault - none99900
Actions:Books
50TBATBANaguluDefault - none99900
Actions:Books
51TBA(None) / ClarkDefault - none99910
Actions:Books
52TBA(None) / AnDefault - none99900
Actions:Books
53TBA(None) / MurchDefault - none99900
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54TBA(None) / LuDefault - none99900
Actions:Books
Label

Home/Ident

A course may be either a “Home” course or an “Ident” course.

A “Home” course is a course that is created, maintained and “owned” by one academic department (aka the “Home” department). The “Home” department is primarily responsible for the decision making and logistical support for the course and instructor.

An “Ident” course is the exact same course as the “Home” (i.e. same instructor, same class time, etc), but is simply being offered to students through another department for purposes of registering under a different department and course number.

Students should, whenever possible, register for their courses under the department number toward which they intend to count the course. For example, an AFAS major should register for the course "Africa: Peoples and Cultures" under its Ident number, L90 306B, whereas an Anthropology major should register for the same course under its Home number, L48 306B.

Grade Options
C=Credit (letter grade)
P=Pass/Fail
A=Audit
U=Satisfactory/Unsatisfactory
S=Special Audit
Q=ME Q (Medical School)

Please note: not all grade options assigned to a course are available to all students, based on prime school and/or division. Please contact the student support services area in your school or program with questions.