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Introduction to Scientific Computation Course

This repo follows Fall2019 track for ETHZ and UZH students.

Lecture and seminar materials for each week are in ./week* folders.

General info

  • Create cloud jupyter session from this repo - Binder
  • Telegram chat room.
  • Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
  • Grading, lateness penalties and other formalities - see this page

Syllabus

  • week00 (18.09.2019) Introduction, Rules, Git
    • Lecture: Code execution lifecycle, compilation vs interpretation, Python, Environments, Git
    • Seminar: Git + python (deadline in 10 days)
  • week01 (25.09.2019) Complexity, Representation of numbers, Stability
    • Lecture: Git, Complexity, Fixed and floating point representations, Vector norms, Stability issues
    • Seminar: numpy, python, linalg, loops, matplotlib (mini)
  • week02 (02.10.2019) Linear systems of equations, SVD, FFT
    • Lecture: Linear systems of equations, SVD, FFT
    • Seminar: comparison of linear systems solvers, svd and applications, fft and applications
  • week03 (09.10.2019) Fourier transform
    • Lecture: Fast Fourier transform
    • Seminar: solving seminar 2 due to high workload
  • week04 (16.10.2019) Learning from data
    • Lecture: Basic definitions, perceptron
    • Seminar: Features, Perceptron, pandas, plots

Contributors & course staff

Course materials and teaching performed by (in random order)

The course is heavily based on the lectures and seminars attended by Mikhail Usvyatsov at different time. Materials is a compilation of resources for courses of:

  • Ivan Oseledets, Numerical Linear Algebra
  • Eugene Zuev, Compilers Construction
  • David Vernon, Algorithms and Data Structures
  • Ivan Tsibulin, Numerical methods
  • Oleg Ponomarev, Introduction to Python
  • Konstantin Vorontsov, Mathematic methods of learning by precedents

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ETHZ 2019 ISC course

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  • Jupyter Notebook 100.0%