Lecture 21 - Jeff Ullman on Getting Rich
Automata with Jeff Ullman
DLS • Jeff Ullman • Data science: Is it real?
Lecture 43 — Collaborative Filtering | Stanford University
Jeffrey David Ullman - Turing Award Winner | Nobel Lectures: English
Locality Sensitive Hashing Part 1, Jeffrey D Ullman
Lecture 20 — Frequent Itemsets | Mining of Massive Datasets | Stanford University
KDD Keynote Talk--On the Nature of Data--Jeffrey D Ullman
Lecture 41 — Overview of Recommender Systems | Stanford University
Lecture 46 — Dimensionality Reduction - Introduction | Stanford University
Lecture 42 — Content Based Recommendations | Stanford University
Lecture 55 — Latent Factor Recommender System | Stanford University
Lecture 39 — Sampling a Stream | Mining of Massive Datasets | Stanford University
Lecture 1 — Distributed File Systems | Stanford University
Lecture 54 — Latent Factor Models | Stanford University
Lecture 24 — Community Detection in Graphs - Motivation | Stanford University
Lecture 47 — Singular Value Decomposition | Stanford University
Lecture 30 — The Graph Laplacian Matrix (Advanced) | Stanford University
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
Lecture 56 — Finding the Latent Factors | Stanford University
Lecture 57 — Extension to Include Global Effects (Advanced) | Stanford University
Lecture 34 — Spectral Clustering Three Steps (Advanced) | Stanford University
Lecture 29 — What Makes a Good Cluster (Advanced) | Stanford University
Lecture 14 — Locality Sensitive Hashing | Stanford University
Lecture 50 — SVD Example and Conclusion | Stanford University
Lecture 16 — Fingerprint Matching | Stanford University
Lecture 44 — Implementing Collaborative Filtering (Advanced) | Stanford University
Lecture 21 — A Priori Algorithm | Mining of Massive Datasets | Stanford University
Lecture 28 — Detecting Communities as Clusters (Advanced) | Stanford University
Lecture 13 — Minhashing | Mining of Massive Datasets | Stanford University
Lecture 32 — Defining the Graph Laplacian (Advanced) | Stanford University
Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University
Lecture 27 — Solving the BIGCLAM | Mining of Massive Datasets | Stanford University
Lecture 70 — Soft Margin SVMs | Mining of Massive Datasets | Stanford University
Lecture 61 — The BFR Algorithm | Mining of Massive Datasets | Stanford University
Lecture 60 — The k Means Algorithm | Stanford University
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
Turing Lecture 2021: Abstractions, Their Algorithms, and Their Compilers
Lecture 23 — All or Most Frequent Itemsets in 2 Passes (Advanced) | Stanford
February 2022 CACM: Abstractions, Their Algorithms, and Their Compilers
Prof. Jeffrey D. Ullman - Honorary Doctorate Recipient at BGU's 46th Board of Governors Meeting
Lecture 59 — Hierarchical Clustering | Stanford University
Interview with Jeffrey Ullman, Founder Great Expectations
Hopcroft on Formal Languages and Their Relationship to Automata.
June 2021 CACM: 2020 ACM A.M. Turing Award
Jeff Ullman - Investor Hot Seat 102920
Jeffrey Ullman y Alfred Aho. Premios Turing 2020
Curb Your Enthusiasm Season 11 Episode 9 Brothers
Curb Your Enthusiasm Season 11 Episode 8 Body Wash
Lecture 45 — Evaluating Recommender Systems | Stanford University
Lecture 49 — SVD Gives the Best Low Rank Approximation (Advanced) | Stanford
Lecture 51 — CUR Decomposition (Advanced) | Stanford University
Lecture 6 — PageRank The Flow Formulation | Stanford University
Lecture 40 — Counting Distinct Elements (Advanced) | Stanford University
Lecture 38 — Bloom Filters | Mining of Massive Datasets | Stanford University
Lecture 19 — Nearest Neighbor Learning | Stanford University
Lecture 5 — Link Analysis and PageRank | Stanford University
Lecture 26 — From AGM to BIGCLAM | Stanford University
OpenHacks | Locality-Sensitive Hashing | Jeffrey Ullman
