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- 0-0 Preamble (3:26)
- 0-1 Revisiting Turing's Test (3:09)
- 0-2 Web-Scale AI and Big Data (3:58)
- 0-3-1 Web Intelligence (3:22)
- 0-3-2 Big Data (6:15)
- 0-4 Course Outline (4:13)
- 0-5 Recap and Preview (2:37)
- 1-1 Basic Indexing (7:22)
- 1-2 Index Creation (5:40)
- 1-3 Complexity of Index Creation (3:28)
- 1-4-1 Ranking - 1 (4:24)
- 1-4-2 Ranking - 2 (4:48)
- 1-5-1 Page Rank and Memory (6:01)
- 1-5-2 Google and the Mind (5:18)
- 1-6-1 Enterprise Search (4:41)
- 1-6-2 Searching Structured Data (6:25)
- 1-7-1 Object Search (5:07)
- 1-7-2 Locality Sensitive Hashing (4:51)
- 1-7-3 LSH Example - 1 (3:17)
- 1-7-4 LSH Example - 2 (1:54)
- 1-7-5 LSH Intuition (4:08)
- 1-7-6 High-dimensional Objects (8:19)
- 1-7-7 Associative Memories (5:25)
- 1-7-8 Recap and Preview (2:44)
- 2-1 Preamble - Listen (3:18)
- 2-2 Shannon Information (6:18)
- 2-3 Information and Advertising (5:49)
- 2-4 TF-IDF (8:24)
- 2-5 TF-IDF Example (6:09)
- 2-6 Language and Information (8:56)
- 2-7 Machine Learning Intro (9:00)
- 2-8-1 Bayes Rule (4:55)
- 2-8-2 Naive Bayes (8:33)
- 2-9 Sentiment Analysis (7:32)
- 2-10 Mutual Information (8:58)
- 2-11 Machine Learning - Limits (10:29)
- 2-12 Recap and Preview (3:14)
- 3-1 Preamble (4:41)
- 3-2 Parallel Computing (8:54)
- 3-3 Map-Reduce (11:49)
- 3-4 Map-Reduce Example in Octo (11:03)
- 3-4-1 Map-Reduce Example in Mincemeat (2:04)
- 3-5 Map-Reduce Applications (13:52)
- 3-6 Parallel Efficiency of Map-Reduce (8:42)
- 3-7 Inside Map-Reduce (9:47)
- 4-0 Preamble (1:43)
- 4-1 Distributed File Systems (12:12)
- 4-2 Database Technology (12:40)
- 4-3 Evolution of Databases (8:52)
- 4-4 Big-Table and HBase (10:08)
- 4-5 NoSQL and Eventual Consistency (12:41)
- 4-6 Future of NoSQL and Dremel (9:28)
- 4-7 Evolution of SQL and Map-Reduce (9:34)
- 4-8 Relational vs Big-Data Technologies (9:11)
- 4-9 Database Trends and Summary (7:22)
- G1 Introduction to Graph Data (11:36)
- G2 Graph Query Languages (14:03)
- G3 Linked Open Data (12:25)
- G4 Challenges and Efficiency (8:56)
- G5 Graph Data Management (10:26)
- G6 Q & A (5:03)
- 4B-1 Iteration and Map-Reduce (8:48)
- 4B-2 Graph Computing (6:50)
- 4B-3 Pregel Model (8:15)
- 4B-4 Page-rank in Pregel (10:35)
- 4B-5 Shortest Paths and Summary (7:43)
- 5-1 Preamble (3:18)
- 5-2 Classification Re-visited (12:56)
- 5-3 Learning Groupings - Clustering (12:13)
- 5-4 Learning Rules (10:11)
- 5-5 Association Rule Mining (8:45)
- 5-6 Learning with Big Data (7:51)
- 5-7 Learning Latent Models (15:57)
- 5-8 Grounded Learning (5:42)
- 5-9 Recap and Preview (2:33)
- 6-1 Preamble (9:07)
- 6-2 Logical Inference (9:56)
- 6-3 Resolution and its Limits (15:58)
- 6-4 Semantic Web (6:18)
- 6-5 Logic and Uncertainty (9:09)
- 6-6 Algebra of Potentials (12:05)
- 6-7 Naive Bayes Revisited (11:26)
- 6-8-1 Bayesian Networks - 1 (9:20)
- 6-8-2 Bayesian Networks - 2 (5:23)
- 6-9 Information Extraction (12:41)
- 6-10 Recap and Preview (7:00)
- 6-11-Programming HW 6 (4:24)
- 7-1 Preamble (2:34)
- 7-2 Linear Prediction (11:00)
- 7-3 Least Squares (12:08)
- 7-4 Nonlinear Models (9:26)
- 7-5 Learning Parameters (12:35)
- 7-6 Prediction Applications (8:30)
- 7-7 Which Technique? (6:13)
- 7-8 Hierarchical Temporal Memory - I (8:39)
- 7-9 Hierarchical Temporal Memory - II (10:38)
- 7-10 Blackboard Architecture (9:06)
- 7-11 Homework Assignment: Genomic Data Analysis
- M1 Motivation (11:59)
- M2 Markov Networks and Logic (8:44)
- M3 Markov Logic via an Example (8:28)
- M4 Markov Logic Formalism (11:39)
- M5 Related Models (10:21)
- M6 Entity Resolution Example - 1 (8:37)
- M7 Entity Resolution Example - 2 (9:53)
- M8 Social Network Analysis using MLN (7:27)
- M9 Research Directions in Markov Logic (6:09)