LEO6006: Data Science and Machine Learning for Aerospace Systems
Course Info.
Course descriptions
This course provides a theoretical foundation for data science and machine learning. It covers key concepts, including regression, classification, probabilistic supervised learning, unsupervised learning, and optimization. Emphasis is placed on understanding the mathematical principles and theoretical frameworks underlying these techniques, with applications to aerospace domains.
Instructors
Lectures
Office hours
Prerequisites
Previous exposure to linear algebra, probability, and programming.
Working knowledge on optimization will be a plus.
Reference textbooks
Grading policy
Lecture Notes
The course material is reproduced from the EE104: Introduction to machine learning by Sanjay Lall and Stephen Boyd at Stanford university, under their kind permission.
Course overview
Predictors
Validation
Features
Empirical risk minimization
Constant predictors
Non-quadratic losses
Non-quadratic regularizers
Neural networks
Classifiers
ERM for classifiers
Boolean classification
Multi-class classification
Probabilistic classification
ERM for probabilistic classification
Unsupervised learning
Principal components analysis
Optimization
Prox-gradient method
Assignments
Several sets of occasional homeworks will be assigned.
You are encouraged to work in groups, however everyone should turn in his/her own work.
Predicting remaining life of NASA turbofan engines (due 5/13)
Files
These are some data and the Python codes in .ipynb notebook files that we are using for lectures or homework assignments.
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