剑桥大学《数据科学: 原理与实践》课程，附PPT下载

该课程将发展数据科学的核心领域(如:模型的回归和分类)从几个角度:概念的形成和属性，解决算法及其实现，数据可视化的探索性数据分析和建模输出的有效表示。课程将以使用Python、scikit-learn和TensorFlow的实践课程作为补充。

• 引言 Introduction. Motivation, applications, examples, common data formats (csv, json), loading data with Python, calculating statistics over a dataset with numpy, logistics and overview of the course.

  
  

• 线性回归 Linear Regression. Defining a model, fitting a model, least squares regression, linear regression, gradient descent, scikit-learn.

  
  

• Practical: Linear Regression

  
  

• 分类 Classification, part I. Classification, logistic regression, perceptron, multi-class classification, classification performance measures.

  
  

• Practical: Classification I

  
  

• Classification, part II. An overview of other classification techniques (e.g., decision trees, SVMs) and more advanced techniques including ensemble-based models (boosting, bagging, exemplified with AdaBoost and Random Forests).

  
  

• Practical: Classification II

  
  

• 深度学习基础 Deep learning basics. Neural networks, applications in the world, optimization, stochastic gradient descent, backpropagation, learning rates

  
  

• TensorFlow深度学习 Deep learning with TensorFlow. Introduction to TensorFlow, minimal TensorFlow example, symbolic graphs, training a network, practical tips for deep learning.

  
  

• Practical: Deep learning with TensorFlow

  
  

• 深度学习架构 Deep learning architectures. Convolutional networks, RNNs, LSTMs, autoencoders, regularization.

  
  

• Practical: Deep learning architectures

  
  

• Visualization, part I. Scales and coordinates, depicting comparisons.

  
  

• Visualization, part II. Common plotting patterns, including dimension reduction.

  
  

• 可视化 Practical: Visualization

  
  

• Challenges in Data Science. Summary of the course, ethics and privacy in data science, P-hacking, look-everywhere effect, bias in the training data, interpretability, information about the hand out test.

https://www.cl.cam.ac.uk/teaching/1920/M20/materials.html

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