教学视频

1-1-Course introduction

1-2-What is big data

1-3-the features and challenges of big data

1-4-Big data applications

1-5-Main tasks of big data

2-1-Preliminary of machine learning

2-2-Loss and model generalization

3-1-Classification-KNN

3-2-Classification-Naive Bayes

3-3-Classification-Decision Tree

4-1-Classification-SVM-1

5-1-Classification-SVM-2

5-2-Ensemble learning

6-1-Clustering-kmeans

6-2-Clustering-Hierarchical clustering

6-3-Clustering-Density based clustering

7-1-Association rule mining

8-1-Preliminary of hashing

8-2-Min-hashing

9-1-Locality-sensitive hashing

10-1-Learn to hashing

10-2-Basics of sampling

10-3-Inverse transform sampling

11-1-Rejection sampling

11-2-Importance sampling

11-3-Markov chain Monte Carlo(MH & Gibbs)-1

12-1-Markov chain Monte Carlo(MH & Gibbs)-2

13-1-Reservoir sampling

13-2-Basics of data stream

14-1-Concept drift detection

14-2-Data stream classification-1

15-1-Data stream classification-2

16-1-Data stream clustering

16-2-Basics of graph minging

17-1-Key node identification

17-2-Community detection

18-1-Architecture of Hadoop

18-2-MapReduce and its key idea-1

19-1-MapReduce and its key idea-2

19-2-Spark and its key idea

19-3-MapReduce V.S. Spark

20-Summary