nycudopcs_advanced

Course material of the Data Science with Python of the Department of Photonics, NYCU

• This is an advanced course for the student who has already passed the introductory course of NYCUDOPCS. However, you are welcome to take this course if you are confident of the following:

  • [x] Programming with class
  • [x] Know how to pass parameters from terminal via argparse
  • [x] Have the ability to use virtualenv for environment control
  • [x] Have the ability to read the documentations of open source packages like numpy, matplotlib, ... etc.

Before you start reading the lectures

All link in the lectures is optimized for reading in VS Code, not for GitHub.

112B 小木屋鬆餅指數

How to update your local repo

cd {path_to_your_repo}/nycudopcs_advanced
git checkout origin/main -- path/to/file

Structures

root
  ├─ Lectures                         # Folder for the handouts and assets of this course
  |      ├─ Lecture01
  |      |     ├─ assets              # Images in the handouts
  |      |     ├─ scripts             # Scripts in the handouts
  |      |     ├─ main_Lecture01.py   # Main script of Lecture 01
  |      |     └─ Lecture01.ipynb     # Lecture 01: Knapsack Problems and Dynamic Programming
  |      |
  |      ├─ Lecture02
  |      |     ├─ assets              # Images in the handouts
  |      |     ├─ scripts             # Scripts in the handouts
  |      |     ├─ main_Lecture02.py   # Main script of Lecture 02
  |      |     └─ Lecture02.ipynb     # Lecture 02: Graph Theory and Graph Optimization Problems
  |      |
  |      ├─ Lecture03
  |      |     ├─ assets              # Images in the handouts
  |      |     ├─ scripts             # Scripts in the handouts
  |      |     ├─ main_Lecture03.py   # Main script of Lecture 03
  |      |     └─ Lecture03.ipynb     # Lecture 03: Random Walks and Stochastic Programs
  |      |
  |      ├─ Lecture04
  |      |     ├─ assets              # Images in the handouts
  |            ├─ data                # Data for Lecture 04
  |      |     ├─ scripts             # Scripts in the handouts
  |      |     ├─ main_Lecture04.py   # Main script of Lecture 04
  |      |     └─ Lecture04.ipynb     # Lecture 04: Monte Carlo Method, Sampling, and Confidence Intervals
  |      |
  |      ├─ Lecture05
  |      |     ├─ assets              # Images in the handouts
  |            ├─ data                # Data for Lecture 05
  |      |     ├─ scripts             # Scripts in the handouts
  |      |     ├─ main_Lecture05.py   # Main script of Lecture 05
  |      |     └─ Lecture05.ipynb     # Lecture 05: Randomized Trials and Hypothesis Checking
  |      |
  |      ├─ Lecture06
  |      |     ├─ 1_Tien_chapter14.01-Basics-of-Linear-Algebra.ipynb
  |      |     ├─ 2_Tien_chapter14.02-Linear-Transformations.ipynb
  |      |     ├─ 3_Tien_chapter14.03-Systems-of-Linear-Equations.ipynb
  |      |     ├─ 4_Tien_chapter14.04(1)-Solutions-to-Systems-of-Linear-Equations.ipynb
  |      |     ├─ 5_Tien_chapter14.04(2)-Solutions-to-Systems-of-Linear-Equations.ipynb
  |      |     └─ 6_Tien_chapter14.05-Solve-Systems-of-Linear-Equations-in-Python.ipynb
  |      |
  |      ├─ Lecture07
  |      |     ├─ Tien_chapter15.01-Eigenvalues-and-Eigenvectors-Problem-Statement.ipynb
  |      |     ├─ Tien_chapter15.02-The-Power-Method.ipynb
  |      |     ├─ Tien_chapter15.03-The-QR-Method.ipynb
  |      |     └─ Tien_chapter15.04-Eigenvalues-and-Eigenvectors-in-Python.ipynb
  |      |
  |      └─ Lecture08
  |            ├─ Tien_chapter16.00-Least-Squares-Regression.ipynb
  |            ├─ Tien_chapter16.01-Least-Squares-Regression-Problem-Statement.ipynb
  |            ├─ Tien_chapter16.02-Least-Squares-Regression-Derivation-Linear-Algebra.ipynb
  |            ├─ Tien_chapter16.03-Least-Squares-Regression-Derivation-Multivariable-Calculus.ipynb
  |            ├─ Tien_chapter16.04-Least-Squares-Regression-in-Python.ipynb
  |            └─ Tien_chapter16.05-Least-Squares-Regression-for-Nonlinear-Functions.ipynb
  |      
  |
  ├─ Archives                         # Archives
  |      ├─ data                      # Data folder
  |      ├─ func.py                   # Functions for simluation
  |      ├─ simOptimizers.py          # Simluation of different optimizers
  |      ├─ Lecture08.ipynb           # Create data for Lecture08.pdf
  |      └─ Lecture08.pdf             # Lecture 08: Introduction to Neural Networks
  | 
  | 
  |
  └─ Readme.md