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ME 462/562 - Spring 2024

Machine Learning for Aerospace

Course Information

Sections Time Location
Undergrad ME 462 (59435)
Graduate- ME 562 (59436)
Graduate- ME 562 (59440)
Online MAX - asynchronous lectures
Mar 18 -May 11
8-week accelerated online program

Online (Canvas)



Wenbin Wan



Luis Quintana


Prerequisites: Proficiency in programming (Python preferred); Knowledge of calculus, probability, and linear algebra (including matrix calculus).

Textbooks: Drafts of the textbooks are available on authors’ websites.

  • [M1] Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy. MIT Press, 2022.
  • [M2] Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy. MIT Press, 2023.
  • [PR] Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2006.

Lecture Handouts: Additional materials will be provided in the form of digital lecture handouts.

Lecture Videos: Each week some concepts will be presented through a collection of video lectures.

Readings: Each week will have assigned readings from the lecture handouts, textbooks, and watching additional videos listed on Canvas. These readings/videos will provide details about all of the concepts for the week, as well as detailed proofs and examples.

Homework Assignments: There will be 5 homework assignments in this course, including

  • 2 written assignments, and
  • 3 coding assignments.

For written assignments, you are encouraged to collaborate and cooperate with your peers on these assignments; however, you should only hand in your own original efforts. Evidence of plagiarism will be dealt with seriously.

For coding assignments, you will be pair with anohter student for Pair Programming. Two students will collaborate on coding assignments to facilitate peer learning, enhance the quality of their code, and promote teamwork in solving problems.

Late homework assignments will not be accepted.

Midterm: One-hour online midterm taken in one continuous attempt during a time window (4/18 3pm - 4/19 3pm MT).

Final Project: The final project is a group design project where you will design and evaluate a machine learning model according to your preference. Submission of the final project can be as a report or a 10-min recorded presentation. Regardless of the format chosen for submission, the decision process, calculations, and computational aspects (including source code) have to be documented and discussed thoroughly.

Quizzes: These quizzes may be attempted an unlimited number of times. The objective is to encourage you to revisit and review the materials repeatedly until you achieve a perfect score of 100%.

Course Assessments:

Assessment Credit Comment
Homeworks 35% 2 homeworks and 3 coding homeworks (7% each)
Midterm 15% One-hour midterm taken in one continuous attempt during a specified time window
Final project 30% Proposal (10%) + Final submission (20%)
Quizzes 15% Online
Participation 5% Online forum discussions (Canvas)


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

  • A+, if \(p \in\) [98,100]
  • 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)

Tentative Topics:

  • Regression
  • SVM
  • Neuron networks
  • Gaussian mixtures
  • Information-theoretic functionals
  • Bayesian inference
  • Maximum likelihood principle
  • Empirical risk minimization
  • Expectation maximization
  • VAEs
  • GANs
  • MDP
  • Q-learning
  • Reinforce
  • Gradient estimator

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.
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 course contents 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.

Page maintained by Wenbin Wan