机器学习与数据挖掘每周500字深度学习·神经网络·计算机视觉

ML4T 总结

2019-04-29  本文已影响40人  我的名字叫清阳

完成了Machine Learning for Trading 课程的期末考试。考试并不算难,TA给了一个复习的纲要。有人做了一个有241到提的题库。把这题库刷一遍,考试就不会有问题。

我把题库刷了几遍,总共用了五六个小时吧。具体也没有记录准确时间。但是,保证自己每道题做对至少两遍。

考试的形式是35分钟30道选择题。

我用了12分钟就完成了。

这门课,不出意外,将是我学习OMSCS的最后一门课程。如果你有兴趣自学,一些资料记录于此,送与后来者:

下面是我自己看视频时候整理的课程笔记:

ML4T 笔记 | 01-03 The power of NumPy

ML4T笔记 | 01-04 Statistical analysis of time series

ML4T笔记 | 01-05 Incomplete data

ML4T笔记 | 01-06 Histograms and scatterplots

ML4T笔记 | 01-07 Sharpe ratio and other portfolio statistics

ML4T笔记 | 01-08 Optimizers: Building a parameterized model

ML4T笔记 | 01-09 Optimizers: How to optimize a portfolio

ML4T笔记 | 03-01 How Machine Learning is used at a hedge fund

ML4T笔记 | 03-02 Regression

ML4T笔记 | 03-03 Assessing a learning algorithm

ML4T笔记 | 03-04 Ensemble learners, bagging and boosting

ML4T笔记 | 02-01 So you want to be a fund manager

ML4T笔记 | 02-02 Market Mechanics

ML4T笔记 | 02-03 What is a company worth?

ML4T笔记 | 02-04 The Capital Asset Pricing Model (CAPM)

ML4T笔记 | 02-05 How hedge funds use the CAPM

ML4T笔记 | 02-06 Technical Analysis

ML4T笔记 | 02-07 Dealing with data

ML4T笔记 | 02-08 The Efficient Markets Hypothesis

ML4T笔记 | 02-09 The Fundamental Law of active portfolio management

ML4T笔记 | 02-10 Portfolio optimization and the efficient frontier

ML4T 笔记 | 03-05 Reinforcement learning

ML4T笔记 | 03-06 Q-Learning

ML4T笔记 | 03-07 Dyna

2019-04-27 初稿
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