Stanford University CS246
In this course, you will learn many of the interesting algorithms that have been developed for efficient processing of large amounts of data in order to extract simple and useful models of that data. These techniques are often used to predict properties of future instances of the same sort of data, or simply to make sense of the data already available. Many people view data mining, or "big data" as machine learning. There are indeed some techniques for processing large datasets that can be considered machine learning, and we shall cover a number of these. But there are also many algorithms and ideas for dealing with big data that are not usually classified as machine learning, and we shall cover many of these as well.
- Jure Leskovec
- Anand Rajaraman
- Jeff Ullman
Be aware that the outline of the course on edX is different from the CS246
- MapReduce
- Link Analysis (PageRank)
- Locality-Sensitive Hashing
- Distance Measures and Nearest-Neighbor Learning
- Frequent Itemset Analysis
- Social-Network Graphs
- Algorithms for Data Streams
- Recommendation Systems
- Dimensionality Reduction
- Clustering
- Computational Advertising
- Machine Learning
- More on MapReduce Algorithms
- More on Locality-Sensitive Hashing
- More on Link Analysis
You can download the textbook through this link
If you are a student and willing to test knowledge on yourself, welcome to use the tool of Gradiance Online Accelerated Learning can register at here and the class token 1EDD8A1D to join the "omnibus class" for the MMDS book.