Date |
Summary |
2018.10.13 |
- syllabus
- python crash course: Stanford Python
- first lecture: welcome to python
- slide link: download
- computation model (CPU-memory)
- variables and naming
- simple data types (int, float, str)
- first built-in data structure: list with slicing
- selection (if-elif-else)
- iteration (for-in, while) with jump statements (break, continue)
- iterators (range, enumerate)
- Monte Carlo method
|
2018.10.17 |
- second lecture: python fundamentals
- slide link: download
- objects: ... everything is an object.
- strings with format 2.0
- file I/O and with-as
- scripts, modules, imports
- third lecture: data structures
- slide link: download
- looping techniques: zip, reverse, sorted
- list/dictionary/tuple/set comprehension
- fourth lecture: functions
- slide link: download
- built-in functions in Python 3.7.x
- user-defined function
- variable scope
- default parameters
- positional/keyword arguments
- variadic positional/keyword arguments
- first-class functions: ... functions are objects.
|
2018.10.20 |
- fifth lecture: functional programming
- slide link: download
- functional programming (map, filter)
- lambda expressions
- generator (yield)
- decorator
- sixth lecture: object-oriented python
- slide link: download
- (FYR) Nina Zakharenko, memory management in python
- user-defined class: attributes and methods
- inheritance
- magic methods
- try-except-else-finally
- raising exceptions
- (FYI) seventh lecture: advanced topics
|
2018.10.24 |
- data acquisition
- data source
- data crawlers
- regular expressions
- beautiful soup
- scrapy
- pandas
|
2018.10.27 |
(class suspended due to personal excuse)
|
2018.10.31 |
- data visualization
- numerical and scientific packages
- time series analysis
|
2017.11.3 |
- exercise
- backtest framework
|
2018.11.7 |
- exercise
- option basics and option pricing models
|
2017.11.10 |
- exercise
- model calibration
- hedging
- performance issues
|
2018.11.14 |
- exercise
- 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) tensorflow tutorials with tensorflow examples
- (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) ... you should figure out mechanisms of models in machine learning...
- (FYR) AlphaGo 2017 ... an inspiring story for recent AI progress...
- (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? ... are you sure that 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?)
- learnable data?
|
2018.11.17 |
- exercise
- blockchain tutorial
|