sherryjyc/isc2019

ETHZ 2019 ISC course

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 -
• 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, MatVec, SVD
  • Lecture: Linear systems of equations, MatVec, SVD
  • Seminar: comparison of linear systems solvers, SVD and applications
  • Due to high worload assignment 2 is extended, the deadline for assignment is extended for 7 days.
• week03 (09.10.2019) Sorting, Fourier transform
  • Lecture: Sorting, Fourier transform
  • Seminar: sorting, FFT
• week04 (16.10.2019) Data related problems
  • Lecture: Supervised learning
  • Seminar: advanced (numpy + matplotlib), pandas, perceptron
• week05 (30.10.2019) Graphs, Graphs algorithms
  • Lecture: Graphs, graphs algorithms
  • Seminar: graphs algorithms
• week06 (06.11.2019) Python intrinsics
  • Lecture: OOP, Python modules, C/C++ in python
  • Seminar: Supervised ML + advanced plots
• week07 (20.11.2019) Symbolic computations, graph computations, sparse matrices
  • Lecture: Symbolic computations, graph computations, sparse matrices
  • Seminar: C3, SWIG, tebnsorflow

Contributors & course staff

Course materials and teaching performed by (in random order)

• Mikhail Usvyatsov - Lectures, materials, seminars

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