| Date |
Summary |
| 2019.2.20 |
- syllabus
- python crash course: Stanford Python
- live code: pyf20190220_class_demo.ipynb
- first lecture: welcome to python
- link: download
- computation model (CPU and memory)
- variables and naming
- simple data types (int, float, str)
- arithmetic operators
- first built-in data structure: list with slicing
- selection (if-elif-else)
- assignment operator and rational operators ... = and == are totally different.
- iteration (for-in, while) with jump statements (break, continue)
- exercise
|
| 2019.2.23 |
- live code: pyf20190223_class_demo.ipynb
- second lecture: data structures
- link: download
- list, tuple, dictionary, and set
- looping techniques: enumerate, zip, reverse, sorted
- list/dictionary/tuple/set comprehension
- (DIY) data structures
- third lecture: functions
- fourth lecture
- link: download
- objects: ... in python, everything is an object.
|
| 2019.2.27 |
|
| 2019.3.6 |
- live code: pyf20190306_class_demo.ipynb
- fifth lecture: functional programming (cont'd)
- sixth lecture: object-oriented programming
- link: download
- (FYR) Nina Zakharenko, memory management in python ... a detailed and comprehensive reading material for the truth under the hood of python.
- user-defined class: attributes and methods
- (single) inheritance
- magic methods
- try-except-else-finally
- raising exceptions
- (DIY) object-oriented python
- (FYI) seventh lecture: advanced topics
- link: download... read pp. 22-45 of this slides for some programming advices in python.
- Spyder
- data acquisition
|
| 2019.3.9 |
- live code: pyf20190309_class_demo.ipynb
- data acquisition (cont'd)
- data crawlers (or you can buy financial data from those famous information suppliers)
- pandas
- data visualization
- homework 1
- upgrade the program twse_crawl.py so that the program could take a stock list as input (you need to check the usage of argparse, shown in the program) and download the data for that stock list, say stock10.csv.
|
| 2019.3.13 |
- live code: pyf20190313_class_demo.ipynb
- data visualization (cont'd)
- backtesting
- numerical and scientific packages
- homework 2
- write a trading strategy and backtest this strategy in backtrader.
|
| 2019.3.16 |
|
| 2019.3.20 |
|
| 2019.3.23 |
|
| 2019.3.27 |
- feedback
- live code: pyf20190327_class_demo.ipynb
- supplementary materials (contributed by William Kuo)
- machine learning tutorial
- (FYR) Pedro Domingos, a few useful things to know about machine learning, University of Washington
- 張鈞閔, hands-on tutorial of machine learning in python (also read introduction to machine learning)
- dimensionality reduction: illustration for principal component analysis (aka PCA) (could be done by singular value decomposition, aka SVD)
- reinforcement learning: flappy bird bot using reinforcement learning in python
- (FYR) machine learning in python: scikit-learn
- (FYR) Gini impurity for dicision tree learning
- (FYR) K-means clustering in python with scikit-learn, DataCamp
- (FYR) essence of linear algebra ... linear algebra is fundamental to CS.
- (FYR) Prof. 林軒田, machine learning foundations (機器學習基石) ... figure out mechanisms of models in machine learning
- (FYR) how AI can save our humanity, Kai-Fu Lee, TED ... a prospective for future AI...
- (FYR) the Turing test: can a computer pass for a human? ... you are not a machine?...
- (FYR) Google Duplex demo from Google IO 2018, Youtube
(read more: 會打電話的 AI 背後：Google Duplex 技術解析, 對答如流的Google Duplex通過了「圖靈測試」嗎?)
- blockchain tutorial
|