課程大綱： 實(shí)用預(yù)測(cè)分析：模型與方法培訓(xùn)
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make
a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
Supervised Learning
Follow a tour through the important methods, algorithms, and techniques in machine learning.
You will learn how these methods build upon each other and can be combined into practical algorithms
that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
Optimization
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to
improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
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