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ME 461/561-012: Speical Topic

Estimation and Learning for Autonomous Systems

Sections Time Location
ME 461-012 & ME 561-012 Tue & Thu 12:30-13:45 Mechanical Engineering 210


Class Time & Space: Tue & Thu 12:30 - 13:45 @ME 210

Course Instructor: Wenbin Wan; email:; office hour: Thu 11am-noon @ME 425

Prerequisites: ME380 and MATH 316 or equivalent, concurrent registration in ME480 preferred; or consent of instructor

Textbook (optional): Barfoot, T. D., State estimation for robotics. Cambridge University Press, 2017.

Learning Objectives:

  • Introduction to key concepts in state estimation and machine learning 
  • Gain basic familiarity with the CPS topics
  • Control/Estimation algorithms
  • Computational models
  • Foundational connections between control/estimation and machine learning
  • Exposure to some most influential ideas in the community

Tentative Topics:

  • Mathematical Background
    • Introduction to optimization
    • Matrix algebra and matrix calculus
    • Linear systems theory
    • Probability theory
  • State estimation
    • Least squares estimation
    • Propagation of states and covariances
    • Discrete-time Kalman Filter
    • Alternate Kalman Filter formulations
    • Verifying Kalman Filter performance
    • Robust Kalman Filtering
    • Nonlinear Kalman filtering
    • Unscented Kalman filter
    • Particle filter
  • Inference and learning
    • Introduction to machine learning
    • Neural networks
    • Stochastic approximation
    • Stochastic gradient descent
    • Monte Carlo methods
    • Importance sampling
    • Parameter estimation via EM algorithm
    • Variational inference
    • Variational lower bound (ELBO)
    • Auto-Encoding Variational Bayes (VAE)
    • Monte Carlo gradient estimation methods in machine learning

Course Assessments:

Assessment Credit Comment
Homeworks 50% 3 writing homeworks and 2 mini coding projects
Participation/attendance 5% 2 unexcused absences
Final project 45% Proposal (15%) + Final report (30%)
Extra credits 5% LaTex’d assignments - 1% each (up to 5%)


The total percentage \(p\)% corresponds the final grades as follows.

  • A+, if \(p \in\) [98,105]
  • A, if \(p \in\) [92,98)
  • A-, if \(p \in\) [90,92)
  • B+, if \(p \in\) [88,90)
  • B, if \(p \in\) [82,88)
  • B-, if \(p \in\) [70,82)
  • C+, if \(p \in\) [68,70)
  • C, if \(p \in\) [62,68)
  • C-, if \(p \in\) [60,62)
  • F, if \(p \in\) [0,60)

Course Policies:

  • You are expected to abide by the University policies on academic honesty and integrity as given in the Student Handbook. Violations of these policies will not be tolerated and are subject to severe sanctions up to and including expulsion from the university.
  • Please show respect for your classmates by limiting distractive behavior. You may use a smart phone, labtop or tablet to take notes or for other academic purposes directly related to the class. Mute your cell phones and other devices, and please keep any side discussions short and quiet.
Work Habits
  • Due dates are non-negotiable, and late work will NOT be accepted!
Other Policies
  • In accordance with university policies, I will make reasonable accommodation to a student’s religious observances and practices due to national origin. If you must miss class because of a feast day or religious holiday, please inform me promptly.
  • UNM is committed to providing equitable access to learning opportunities for students with documented disabilities. As your instructor, it is my objective to facilitate an inclusive classroom setting, in which students have full access and opportunity to participate. To engage in a confidential conversation about the process for requesting reasonable accommodations for this class and/or program, please contact Accessibility Resource Center at or by phone at 505-277-3506.


Dates Topics Materials Comments
Week 1
(8/22 8/24)
Syllabus reading
Optimization review
Week 2
(8/29 8/31)
Matrix calculus
Linear systems
Week 3
(9/05 9/07)
Probability review
Least squares estimation
Week 4
(9/12 9/14)
Propagation of states and covariances
Discrete-time Kalman Filter
HW1 Due @Sat. 11pm
Week 5
(9/19 9/21)
Alternate KF formulations
Verifying KF performance
Week 6
(9/26 9/28)
Robust Kalman filtering
Nonlinear Kalman filtering
HW2 Due @Sat. 11pm
Week 7
(10/03 10/05)
Unscented Kalman filter
Particle filter
Week 8
(10/10 10/12)
Introduction to machine learning [15.pdf] 10/12: No class - Fall break
Week 9
(10/17 10/19)
Neural networks
Stochastic approximation
HW3 Due @Sat. 11pm
Week 10
(10/24 10/26)
Stochastic gradient descent
Monte Carlo methods
Initialize your project proposal
Week 11
(10/31 11/04)
Importance sampling
EM algorithm
HW4 Due @Sat. 11pm
Week 12
(11/07 11/09)
Parameter estimation via EM algorithm
Variational inference
Week 13
(11/14 11/16)
Variational lower bound (ELBO) [24.pdf] 11/16: Project proposal discussion
Week 14
(11/21 11/23)
Auto-Encoding Variational Bayes (VAE) [25.pdf] 11/23: No class - Thanksgiving
Week 15
(11/28 11/30)
Monte Carlo gradient estimation methods in machine learning \(\times\) 2 [26.pdf]
HW5 Due @Sat. 11pm
Week 16
(12/05 12/07)
Final project presentation
Final Exam Week
Final report Due

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