ASE3001: Computation Lab. 
 
Course Info.
Course descriptions  
This course covers elementary computational techniques for solving mathematical problems in aerospace engineering and other engineering disciplines. Students will use high level programming languages to formulate, interpret and analyze practical real-world problems encountered at a wide variety of engineering disciplines. Covered topics include but not limited to differential equations, linear algebra, probability, Fourier transform, introductory machine learning and artificial intelligence. 
 
 
Instructors  
Lectures  
Office hours 
Prerequisites  
Reference textbooks 
  
Grading policy 
Lecture Notes
The link directs to the associated Jupyter notebook, which opens on Google Colaboratory when the “Open in Colab” button is clicked. 
A very short Python review with Numpy, Matplotlib, and Pandas modules (Files: kfxsim.csv) 
 
Differential equations and dynamical systems 
 
Image processing 
 
Monte-Carlo methods 
 
Discrete Fourier transform 
 
Bayesian inference 
 
Signal processing 
 
Optimization 
 
Control design 
 
Rocket guidance 
 
Machine learning 
 
Deep learning
 
 
 
Assignments
Assignments will be up with the lab session, during which the students start to work on them. Completed works should be turned in by next week's lecture to the course TAs. 
Warm-up data exploration and visualization (due 9/10) 
 
Numerical simulation (due 9/17) 
 
Image filtering (due 9/24) 
 
Impact distribution (due 10/1) 
 
No surprises (due 10/8) 
 
Mobile phone localization (due 10/22) 
 
State estimation and filtering (due 10/29) 
 
 
Exams
Midterm exam (2020 Autumn) (solution) 
 
Final exam (2020 Autumn) 
 
 
 
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