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2 - 1 - Distributed File Systems (15-50).mp4
2 - 10 - Why Teleports Solve the Problem (12-26).mp4
2 - 11 - How we Really Compute PageRank (13-49).mp4
2 - 2 - The MapReduce Computational Model (22-04).mp4
2 - 3 - Scheduling and Data Flow (12-43).mp4
2 - 4 - Combiners and Partition Functions (12-17) [Advanced].mp4
2 - 5 - Link Analysis and PageRank (9-39).mp4
2 - 6 - PageRank- The Flow Formulation (9-16).mp4
2 - 7 - PageRank- The Matrix Formulation (8-02).mp4
2 - 8 - PageRank- Power Iteration (10-34).mp4
2 - 9 - PageRank- The Google Formulation (12-08).mp4
3 - 1 - Finding Similar Sets (13-37).mp4
3 - 10 - A-Priori Algorithm (13-07).mp4
3 - 11 - Improvements to A-Priori (17-26) [Advanced].mp4
3 - 12 - All or Most Frequent Itemsets in 2 Passes (14-40) [Advanced].mp4
3 - 2 - Minhashing (25-18).mp4
3 - 3 - Locality-Sensitive Hashing (19-24).mp4
3 - 4 - Applications of LSH (11-40).mp4
3 - 5 - Fingerprint Matching (7-07).mp4
3 - 6 - Finding Duplicate News Articles (6-08).mp4
3 - 7 - Distance Measures (22-39).mp4
3 - 8 - Nearest Neighbor Learning (11-39).mp4
3 - 9 - Frequent Itemsets (29-50).mp4
4 - 1 - Community Detection in Graphs- Motivation (5-44).mp4
4 - 10 - Spectral Graph Partitioning- Finding a Partition (13-25) [Advanced].mp4
4 - 11 - Spectral Clustering- Three Steps (7-17) [Advanced].mp4
4 - 12 - Analysis of Large Graphs- Trawling (9-02) [Advanced].mp4
4 - 13 - Mining Data Streams (12-01).mp4
4 - 14 - Counting 1-'s (29-00) [Advanced].mp4
4 - 15 - Bloom Filters (18-00).mp4
4 - 16 - Sampling a Stream (11-30).mp4
4 - 17 - Counting Distinct Elements (25-59) [Advanced].mp4
4 - 2 - The Affiliation Graph Model (10-04).mp4
4 - 3 - From AGM to BIGCLAM (8-48).mp4
4 - 4 - Solving the BIGCLAM (9-19).mp4
4 - 5 - Detecting Communities as Clusters (8-39) [Advanced].mp4
4 - 6 - What Makes a Good Cluster- (8-48) [Advanced].mp4
4 - 7 - The Graph Laplacian Matrix (6-51) [Advanced].mp4
4 - 8 - Examples of Eigendecompositions of Graphs (6-16) [Advanced].mp4
4 - 9 - Defining the Graph Laplacian (3-27) [Advanced].mp4
5 - 1 - Overview of Recommender Systems (16-51).mp4
5 - 10 - Dimensionality Reduction- Introduction (12-01).mp4
5 - 11 - Singular-Value Decomposition (13-39).mp4
5 - 12 - Dimensionality Reduction with SVD (9-04).mp4
5 - 13 - SVD Gives the Best Low-Rank Approximation (8-28) [Advanced].mp4
5 - 14 - SVD Example and Conclusion (11-58).mp4
5 - 15 - CUR Decomposition (6-27) [Advanced].mp4
5 - 16 - The CUR Algorithm (6-15) [Advanced].mp4
5 - 17 - Discussion of the CUR Method (7-09).mp4
5 - 2 - Content-Based Recommendations (21-00).mp4
5 - 3 - Collaborative Filtering (20-52).mp4
5 - 4 - Implementing Collaborative Filtering (13-46) [Advanced].mp4
5 - 5 - Evaluating Recommender Systems (6-09).mp4
5 - 6 - Latent-Factor Models (16-11).mp4
5 - 7 - Latent-Factor Recommender System (14-16).mp4
5 - 8 - Finding the Latent Factors (13-20).mp4
5 - 9 - Extension to Include Global Effects (9-42) [Advanced].mp4
6 - 1 - Overview of Clustering (8-46).mp4
6 - 2 - Hierarchical Clustering (14-07).mp4
6 - 3 - The k-Means Algorithm (12-49).mp4
6 - 4 - The BFR Algorithm (25-01).mp4
6 - 5 - The CURE Algorithm (15-13) [Advanced].mp4
6 - 6 - Computational Advertising- Bipartite Graph Matching (24-47).mp4
6 - 7 - The AdWords Problem (19-21).mp4
6 - 8 - The Balance Algorithm (15-16).mp4
6 - 9 - Generalized Balance (14-35) [Advanced].mp4
7 - 1 - Support Vector Machines- Introduction (7-30).mp4
7 - 10 - Building Decision Trees Using MapReduce (8-14) [Advanced].mp4
7 - 11 - Decision Trees- Conclusion (7-25).mp4
7 - 12 - MapReduce Algorithms Part I (10-51) [Advanced].mp4
7 - 13 - MapReduce Algorithms Part II (9-46) [Advanced].mp4
7 - 14 - Theory of MapReduce Algorithms (19-39) [Advanced].mp4
7 - 15 - Matrix Multiplication in MapReduce (24-48) [Advanced].mp4
7 - 2 - Support Vector Machines- Mathematical Formulation (12-15).mp4
7 - 3 - What is the Margin- (8-22).mp4
7 - 4 - Soft-Margin SVMs (9-46).mp4
7 - 5 - How to Compute the Margin (14-36) [Advanced].mp4
7 - 6 - Support Vector Machines- Example (7-07).mp4
7 - 7 - Decision Trees (8-33).mp4
7 - 8 - How to Construct a Tree (13-21).mp4
7 - 9 - Information Gain (9-50).mp4
8 - 1 - LSH Families (21-13).mp4
8 - 10 - Hubs and Authorities (15-16) [Advanced].mp4
8 - 11 - Web Spam- Introduction (6-50).mp4
8 - 12 - Spam Farms (8-00).mp4
8 - 13 - TrustRank (10-05).mp4
8 - 2 - More About LSH Families (12-57).mp4
8 - 3 - Sets and Strings With a High Degree of Similarity (11-29) [Advanced].mp4
8 - 4 - Prefix of a String (7-43) [Advanced].mp4
8 - 5 - Positions Within Prefixes (14-04) [Advanced].mp4
8 - 6 - Exploiting Length (14-39) [Advanced].mp4
8 - 7 - Computing PageRank on Big Graphs (10-18) [Advanced].mp4
8 - 8 - Topic-Specific PageRank (10-06).mp4
8 - 9 - Application to Measuring Proximity in Graphs (6-25).mp4
Mining_Massive_Datasets_Stanford_slides.zip
Mining_Massive_Datasets_Stanford_subtitles.zip

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