Aula #1 - 1. Distributed File Systems | Stanford University
Aula #2 - 2. The MapReduce Computational Model | Stanford University
Aula #3 - 3. Scheduling and Data Flow | Stanford University
Aula #4 - 4. Combiners and Partition Functions (Advanced) | Stanford University
Aula #5 - 5. Link Analysis and PageRank | Stanford University
Aula #6 - 6. PageRank The Flow Formulation | Stanford University
Aula #7 - 7. PageRank The Matrix Formulation | Stanford University
Aula #8 - 8. PageRank Power Iteration | Stanford University
Aula #9 - PageRank - The Google Formulation | Stanford University
Aula #10 - Why Teleports Solve the Problem | Stanford University
Aula #11 - How we Really Compute PageRank | Stanford University
Aula #12 - Finding Similar Sets | Stanford University
Aula #13 - Minhashing | Mining of Massive Datasets | Stanford University
Aula #14 - Locality Sensitive Hashing | Stanford University
Aula #15 - Applications of LSH | Stanford University
Aula #16 - Fingerprint Matching | Stanford University
Aula #17 - Finding Duplicate News Articles | Stanford University
Aula #18 - Distance Measures | Mining of Massive Datasets | Stanford University
Aula #19 - Nearest Neighbor Learning | Stanford University
Aula #20 - Frequent Itemsets | Mining of Massive Datasets | Stanford University
Aula #21 - A Priori Algorithm | Mining of Massive Datasets | Stanford University
Aula #22 - Improvements to A Priori (Advanced) | Stanford University
Aula #23 - All or Most Frequent Itemsets in 2 Passes (Advanced) | Stanford
Aula #24 - Community Detection in Graphs - Motivation | Stanford University
Aula #25 - The Affiliation Graph Model | Stanford University
Aula #26 - From AGM to BIGCLAM | Stanford University
Aula #27 - Solving the BIGCLAM | Mining of Massive Datasets | Stanford University
Aula #28 - Detecting Communities as Clusters (Advanced) | Stanford University
Aula #29 - What Makes a Good Cluster (Advanced) | Stanford University
Aula #30 - The Graph Laplacian Matrix (Advanced) | Stanford University
Aula #31 - Examples of Eigendecompositions of Graphs (Advanced) | Stanford
Aula #32 - Defining the Graph Laplacian (Advanced) | Stanford University
Aula #33 - Spectral Graph Partitioning Finding a Partition (Advanced) | Stanford
Aula #34 - Spectral Clustering Three Steps (Advanced) | Stanford University
Aula #35 - Analysis of Large Graphs Trawling (Advanced) | Stanford University
Aula #36 - Mining Data Streams | Mining of Massive Datasets | Stanford University
Aula #37 - Counting 1 's (Advanced) | Mining of Massive Datasets | Stanford University
Aula #38 - Bloom Filters | Mining of Massive Datasets | Stanford University
Aula #39 - Sampling a Stream | Mining of Massive Datasets | Stanford University
Aula #40 - Counting Distinct Elements (Advanced) | Stanford University
Aula #41 - Overview of Recommender Systems | Stanford University
Aula #42 - Content Based Recommendations | Stanford University
Aula #43 - Collaborative Filtering | Stanford University
Aula #44 - Implementing Collaborative Filtering (Advanced) | Stanford University
Aula #45 - Evaluating Recommender Systems | Stanford University
Aula #46 - Dimensionality Reduction - Introduction | Stanford University
Aula #47 - Singular Value Decomposition | Stanford University
Aula #48 - Dimensionality Reduction with SVD | Stanford University
Aula #49 - SVD Gives the Best Low Rank Approximation (Advanced) | Stanford
Aula #50 - SVD Example and Conclusion | Stanford University
Aula #51 - CUR Decomposition (Advanced) | Stanford University
Aula #52 - The CUR Algorithm (Advanced) | Stanford University
Aula #53 - Discussion of the CUR Method | Stanford University
Aula #54 - Latent Factor Models | Stanford University
Aula #55 - Latent Factor Recommender System | Stanford University
Aula #56 - Finding the Latent Factors | Stanford University
Aula #57 - Extension to Include Global Effects (Advanced) | Stanford University
Aula #58 - Overview of Clustering | Mining of Massive Datasets | Stanford University
Aula #59 - Hierarchical Clustering | Stanford University
Aula #60 - The k Means Algorithm | Stanford University
Aula #61 - The BFR Algorithm | Mining of Massive Datasets | Stanford University
Aula #62 - The CURE Algorithm (Advanced) | Stanford University
Aula #63 - Computational Advertising Bipartite Graph Matching | Stanford
Aula #64 - The AdWords Problem | Mining of Massive Datasets | Stanford University
Aula #65 - The Balance Algorithm | Mining of Massive Datasets | Stanford University
Aula #66 - Generalized Balance (Advanced) | Stanford University
Aula #67 - Support Vector Machines - Introduction | Stanford University
Aula #68 - Support Vector Machines Mathematical Formulation | Stanford
Aula #69 - What is the Margin | Mining of Massive Datasets | Stanford University
Aula #70 - Soft Margin SVMs | Mining of Massive Datasets | Stanford University
Aula #71 - How to Compute the Margin (Advanced) | Stanford University
Aula #72 - Support Vector Machines - Example | Stanford University
Aula #73 - Decision Trees | Mining of Massive Datasets | Stanford University
Aula #74 - How to Construct a Tree | Stanford University
Aula #75 - Information Gain | Mining of Massive Datasets | Stanford University
Aula #76 - Building Decision Trees Using MapReduce (Advanced ) | Stanford
Aula #77 - Decision Trees - Conclusion | Stanford University
Aula #78 - MapReduce Algorithms Part I (Advanced) | Stanford University
Aula #79 - MapReduce Algorithms Part II | (Advanced) | Stanford University
Aula #80 - Theory of MapReduce Algorithms (Advanced) | Stanford University
Aula #81 - Matrix Multiplication in MapReduce (Advanced) | Stanford University
Aula #82 - LSH Families | Mining of Massive Datasets | Stanford University
Aula #83 - More About LSH Families | Stanford University
Aula #84 - Sets and Strings With a High Degree of Similarity (Advanced) | Stanford
Aula #85 - Prefix of a String (Advanced) | Stanford University
Aula #86 - Positions Within Prefixes (Advanced) | Stanford University
Aula #87 - Exploiting Length (Advanced) | Stanford University
Aula #88 - Computing PageRank on Big Graphs (Advanced) | Stanford University
Aula #89 - Topic Specific PageRank | Stanford University
Aula #90 - Application to Measuring Proximity in Graphs | Stanford University
Aula #91 - Hubs and Authorities (Advanced) | Stanford University
Aula #92 - Web Spam - Introduction | Mining of Massive Datasets | Stanford University
Aula #93 - Spam Farms | Mining of Massive Datasets | Stanford University
Aula #94 - Trust Rank | Mining of Massive Datasets | Stanford University