Mining Massive Datasets

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Mining Massive Datasets

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes. We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair. When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches. Many other large-scale algorithms are covered as well, as outlined in the course syllabus.

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created on

11/12/2017

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Syllabus:

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
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Modules & lessons

Module 1: Mining Massive Datasets

Creating a class flash text

Lesson 1.1 : Introdução, Fonte e Autoria

Creating a class flash video

Lesson 1.2 : 1. Distributed File Systems | Stanford University

Creating a class flash video

Lesson 1.3 : 2. The MapReduce Computational Model | Stanford University

Creating a class flash video

Lesson 1.4 : 3. Scheduling and Data Flow | Stanford University

Creating a class flash video

Lesson 1.5 : 4. Combiners and Partition Functions (Advanced) | Stanford University

Creating a class flash video

Lesson 1.6 : 5. Link Analysis and PageRank | Stanford University

Creating a class flash video

Lesson 1.7 : 6. PageRank The Flow Formulation | Stanford University

Creating a class flash video

Lesson 1.8 : 7. PageRank The Matrix Formulation | Stanford University

Creating a class flash video

Lesson 1.9 : 8. PageRank Power Iteration | Stanford University

Creating a class flash video

Lesson 1.10 : PageRank - The Google Formulation | Stanford University

Creating a class flash video

Lesson 1.11 : Why Teleports Solve the Problem | Stanford University

Creating a class flash video

Lesson 1.12 : How we Really Compute PageRank | Stanford University

Creating a class flash video

Lesson 1.13 : Finding Similar Sets | Stanford University

Creating a class flash video

Lesson 1.14 : Minhashing | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.15 : Locality Sensitive Hashing | Stanford University

Creating a class flash video

Lesson 1.16 : Applications of LSH | Stanford University

Creating a class flash video

Lesson 1.17 : Fingerprint Matching | Stanford University

Creating a class flash video

Lesson 1.18 : Finding Duplicate News Articles | Stanford University

Creating a class flash video

Lesson 1.19 : Distance Measures | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.20 : Nearest Neighbor Learning | Stanford University

Creating a class flash video

Lesson 1.21 : Frequent Itemsets | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.22 : A Priori Algorithm | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.23 : Improvements to A Priori (Advanced) | Stanford University

Creating a class flash video

Lesson 1.24 : All or Most Frequent Itemsets in 2 Passes (Advanced) | Stanford

Creating a class flash video

Lesson 1.25 : Community Detection in Graphs - Motivation | Stanford University

Creating a class flash video

Lesson 1.26 : The Affiliation Graph Model | Stanford University

Creating a class flash video

Lesson 1.27 : From AGM to BIGCLAM | Stanford University

Creating a class flash video

Lesson 1.28 : Solving the BIGCLAM | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.29 : Detecting Communities as Clusters (Advanced) | Stanford University

Creating a class flash video

Lesson 1.30 : What Makes a Good Cluster (Advanced) | Stanford University

Creating a class flash video

Lesson 1.31 : The Graph Laplacian Matrix (Advanced) | Stanford University

Creating a class flash video

Lesson 1.32 : Examples of Eigendecompositions of Graphs (Advanced) | Stanford

Creating a class flash video

Lesson 1.33 : Defining the Graph Laplacian (Advanced) | Stanford University

Creating a class flash video

Lesson 1.34 : Spectral Graph Partitioning Finding a Partition (Advanced) | Stanford

Creating a class flash video

Lesson 1.35 : Spectral Clustering Three Steps (Advanced) | Stanford University

Creating a class flash video

Lesson 1.36 : Analysis of Large Graphs Trawling (Advanced) | Stanford University

Creating a class flash video

Lesson 1.37 : Mining Data Streams | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.38 : Counting 1 's (Advanced) | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.39 : Bloom Filters | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.40 : Sampling a Stream | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.41 : Counting Distinct Elements (Advanced) | Stanford University

Creating a class flash video

Lesson 1.42 : Overview of Recommender Systems | Stanford University

Creating a class flash video

Lesson 1.43 : Content Based Recommendations | Stanford University

Creating a class flash video

Lesson 1.44 : Collaborative Filtering | Stanford University

Creating a class flash video

Lesson 1.45 : Implementing Collaborative Filtering (Advanced) | Stanford University

Creating a class flash video

Lesson 1.46 : Evaluating Recommender Systems | Stanford University

Creating a class flash video

Lesson 1.47 : Dimensionality Reduction - Introduction | Stanford University

Creating a class flash video

Lesson 1.48 : Singular Value Decomposition | Stanford University

Creating a class flash video

Lesson 1.49 : Dimensionality Reduction with SVD | Stanford University

Creating a class flash video

Lesson 1.50 : SVD Gives the Best Low Rank Approximation (Advanced) | Stanford

Creating a class flash video

Lesson 1.51 : SVD Example and Conclusion | Stanford University

Creating a class flash video

Lesson 1.52 : CUR Decomposition (Advanced) | Stanford University

Creating a class flash video

Lesson 1.53 : The CUR Algorithm (Advanced) | Stanford University

Creating a class flash video

Lesson 1.54 : Discussion of the CUR Method | Stanford University

Creating a class flash video

Lesson 1.55 : Latent Factor Models | Stanford University

Creating a class flash video

Lesson 1.56 : Latent Factor Recommender System | Stanford University

Creating a class flash video

Lesson 1.57 : Finding the Latent Factors | Stanford University

Creating a class flash video

Lesson 1.58 : Extension to Include Global Effects (Advanced) | Stanford University

Creating a class flash video

Lesson 1.59 : Overview of Clustering | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.60 : Hierarchical Clustering | Stanford University

Creating a class flash video

Lesson 1.61 : The k Means Algorithm | Stanford University

Creating a class flash video

Lesson 1.62 : The BFR Algorithm | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.63 : The CURE Algorithm (Advanced) | Stanford University

Creating a class flash video

Lesson 1.64 : Computational Advertising Bipartite Graph Matching | Stanford

Creating a class flash video

Lesson 1.65 : The AdWords Problem | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.66 : The Balance Algorithm | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.67 : Generalized Balance (Advanced) | Stanford University

Creating a class flash video

Lesson 1.68 : Support Vector Machines - Introduction | Stanford University

Creating a class flash video

Lesson 1.69 : Support Vector Machines Mathematical Formulation | Stanford

Creating a class flash video

Lesson 1.70 : What is the Margin | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.71 : Soft Margin SVMs | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.72 : How to Compute the Margin (Advanced) | Stanford University

Creating a class flash video

Lesson 1.73 : Support Vector Machines - Example | Stanford University

Creating a class flash video

Lesson 1.74 : Decision Trees | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.75 : How to Construct a Tree | Stanford University

Creating a class flash video

Lesson 1.76 : Information Gain | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.77 : Building Decision Trees Using MapReduce (Advanced ) | Stanford

Creating a class flash video

Lesson 1.78 : Decision Trees - Conclusion | Stanford University

Creating a class flash video

Lesson 1.79 : MapReduce Algorithms Part I (Advanced) | Stanford University

Creating a class flash video

Lesson 1.80 : MapReduce Algorithms Part II | (Advanced) | Stanford University

Creating a class flash video

Lesson 1.81 : Theory of MapReduce Algorithms (Advanced) | Stanford University

Creating a class flash video

Lesson 1.82 : Matrix Multiplication in MapReduce (Advanced) | Stanford University

Creating a class flash video

Lesson 1.83 : LSH Families | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.84 : More About LSH Families | Stanford University

Creating a class flash video

Lesson 1.85 : Sets and Strings With a High Degree of Similarity (Advanced) | Stanford

Creating a class flash video

Lesson 1.86 : Prefix of a String (Advanced) | Stanford University

Creating a class flash video

Lesson 1.87 : Positions Within Prefixes (Advanced) | Stanford University

Creating a class flash video

Lesson 1.88 : Exploiting Length (Advanced) | Stanford University

Creating a class flash video

Lesson 1.89 : Computing PageRank on Big Graphs (Advanced) | Stanford University

Creating a class flash video

Lesson 1.90 : Topic Specific PageRank | Stanford University

Creating a class flash video

Lesson 1.91 : Application to Measuring Proximity in Graphs | Stanford University

Creating a class flash video

Lesson 1.92 : Hubs and Authorities (Advanced) | Stanford University

Creating a class flash video

Lesson 1.93 : Web Spam - Introduction | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.94 : Spam Farms | Mining of Massive Datasets | Stanford University

Creating a class flash video

Lesson 1.95 : Trust Rank | Mining of Massive Datasets | Stanford University

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