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[Coursera] Web Intelligence and Big Data (Indian Institute of Technology Delhi) (bigdata)

by: Indian Institute of Technology Delhi

Publisher: Academic Torrents

Contributor: Academic Torrents

Academictorrents_collection: video-lectures

Addeddate: 2018-08-12 18:00:09

External-identifier: urn:academictorrents:8921ec4d2076e8a6e56a2387d5157aa7c0ef7f10

Identifier: academictorrents_8921ec4d2076e8a6e56a2387d5157aa7c0ef7f10

Ocr: ABBYY FineReader 11.0 (Extended OCR)

Ppi: 300

Scanner: Internet Archive Python library 1.8.1

Source: http://academictorrents.com/details/8921ec4d2076e8a6e56a2387d5157aa7c0ef7f10
torrent:urn:sha1:8921ec4d2076e8a6e56a2387d5157aa7c0ef7f10

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20 files

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BACK

36.4K

AbouttheInstructor.html

36.6K

CourseOutline.html

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DiscussionForums.html

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Home.html

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QuickQuestions15.html

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Quizzes.html

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0_Quiz_0.html

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assignment_home.html

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ITEM TILE

107 files

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01_0-0_Preamble_3-26.mp4

4.2M

02_0-1_Revisiting_Turings_Test_3-09.mp4

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03_0-2_Web-Scale_AI_and_Big_Data_3-58.mp4

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04_0-3-1_Web_Intelligence_3-22.mp4

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05_0-3-2_Big_Data_6-15.mp4

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06_0-4_Course_Outline_4-13.mp4

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07_0-5_Recap_and_Preview_2-37.mp4

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01_1-1_Basic_Indexing_7-22.mp4

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02_1-2_Index_Creation_5-40.mp4

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03_1-3_Complexity_of_Index_Creation_3-28.mp4

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04_1-4-1_Ranking_-_1_4-24.mp4

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05_1-4-2_Ranking_-_2_4-48.mp4

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06_1-5-1_Page_Rank_and_Memory_6-01.mp4

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07_1-5-2_Google_and_the_Mind_5-18.mp4

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08_1-6-1_Enterprise_Search_4-41.mp4

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09_1-6-2_Searching_Structured_Data_6-25.mp4

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10_1-7-1_Object_Search_5-07.mp4

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11_1-7-2_Locality_Sensitive_Hashing_4-51.mp4

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01_2-1_Preamble_-_Listen_3-18.mp4

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02_2-2_Shannon_Information_6-18.mp4

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03_2-3_Information_and_Advertising_5-49.mp4

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04_2-4_TF-IDF_8-24.mp4

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05_2-5_TF-IDF_Example_6-09.mp4

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06_2-6_Language_and_Information_8-56.mp4

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07_2-7_Machine_Learning_Intro_9-00.mp4

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08_2-8-1_Bayes_Rule_4-55.mp4

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09_2-8-2_Naive_Bayes_8-33.mp4

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10_2-9_Sentiment_Analysis_7-32.mp4

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11_2-10_Mutual_Information_8-58.mp4

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13_2-12_Recap_and_Preview_3-14.mp4

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01_3-1_Preamble_4-41.mp4

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02_3-2_Parallel_Computing_8-54.mp4

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03_3-3_Map-Reduce_11-49.mp4

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04_3-4_Map-Reduce_Example_in_Octo_11-03.mp4

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06_3-5_Map-Reduce_Applications_13-52.mp4

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07_3-6_Parallel_Efficiency_of_Map-Reduce_8-42.mp4

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08_3-7_Inside_Map-Reduce_9-47.mp4

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01_4-0_Preamble_1-43.mp4

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02_4-1_Distributed_File_Systems_12-12.mp4

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03_4-2_Database_Technology_12-40.mp4

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04_4-3_Evolution_of_Databases_8-52.mp4

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05_4-4_Big-Table_and_HBase_10-08.mp4

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06_4-5_NoSQL_and_Eventual_Consistency_12-41.mp4

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07_4-6_Future_of_NoSQL_and_Dremel_9-28.mp4

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08_4-7_Evolution_of_SQL_and_Map-Reduce_9-34.mp4

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09_4-8_Relational_vs_Big-Data_Technologies_9-11.mp4

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10_4-9_Database_Trends_and_Summary_7-22.mp4

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01_G1_Introduction_to_Graph_Data_11-36.mp4

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02_G2_Graph_Query_Languages_14-03.mp4

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03_G3_Linked_Open_Data_12-25.mp4

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04_G4_Challenges_and_Efficiency_8-56.mp4

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05_G5_Graph_Data_Management_10-26.mp4

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01_4B-1_Iteration_and_Map-Reduce_8-48.mp4

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02_4B-2_Graph_Computing_6-50.mp4

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03_4B-3_Pregel_Model_8-15.mp4

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04_4B-4_Page-rank_in_Pregel_10-35.mp4

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05_4B-5_Shortest_Paths_and_Summary_7-43.mp4

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01_5-1_Preamble_3-18.mp4

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02_5-2_Classification_Re-visited_12-56.mp4

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03_5-3_Learning_Groupings_-_Clustering_12-13.mp4

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04_5-4_Learning_Rules_10-11.mp4

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05_5-5_Association_Rule_Mining_8-45.mp4

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06_5-6_Learning_with_Big_Data_7-51.mp4

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07_5-7_Learning_Latent_Models_15-57.mp4

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08_5-8_Grounded_Learning_5-42.mp4

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09_5-9_Recap_and_Preview_2-33.mp4

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01_6-1_Preamble_9-07.mp4

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02_6-2_Logical_Inference_9-56.mp4

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03_6-3_Resolution_and_its_Limits_15-58.mp4

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04_6-4_Semantic_Web_6-18.mp4

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05_6-5_Logic_and_Uncertainty_9-09.mp4

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06_6-6_Algebra_of_Potentials_12-05.mp4

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07_6-7_Naive_Bayes_Revisited_11-26.mp4

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08_6-8-1_Bayesian_Networks_-_1_9-20.mp4

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09_6-8-2_Bayesian_Networks_-_2_5-23.mp4

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10_6-9_Information_Extraction_12-41.mp4

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12_6-11-Programming_HW_6_4-24.mp4

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01_7-1_Preamble_2-34.mp4

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02_7-2_Linear_Prediction_11-00.mp4

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03_7-3_Least_Squares_12-08.mp4

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04_7-4_Nonlinear_Models_9-26.mp4

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05_7-5_Learning_Parameters_12-35.mp4

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06_7-6_Prediction_Applications_8-30.mp4

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07_7-7_Which_Technique_6-13.mp4

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08_7-8_Hierarchical_Temporal_Memory_-_I_8-39.mp4

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09_7-9_Hierarchical_Temporal_Memory_-_II_10-38.mp4

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10_7-10_Blackboard_Architecture_9-06.mp4

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01_M1_Motivation_11-59.mp4

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02_M2_Markov_Networks_and_Logic_8-44.mp4

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03_M3_Markov_Logic_via_an_Example_8-28.mp4

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04_M4_Markov_Logic_Formalism_11-39.mp4

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05_M5_Related_Models_10-21.mp4

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06_M6_Entity_Resolution_Example_-_1_8-37.mp4

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07_M7_Entity_Resolution_Example_-_2_9-53.mp4

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08_M8_Social_Network_Analysis_using_MLN_7-27.mp4

8.0M

09_M9_Research_Directions_in_Markov_Logic_6-09.mp4

11.5M

01_Course_Recap_and_Pointers_9-34.mp4

107 files

OGG VIDEO

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01_0-0_Preamble_3-26.ogv

12.7M

02_0-1_Revisiting_Turings_Test_3-09.ogv

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03_0-2_Web-Scale_AI_and_Big_Data_3-58.ogv

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04_0-3-1_Web_Intelligence_3-22.ogv

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05_0-3-2_Big_Data_6-15.ogv

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06_0-4_Course_Outline_4-13.ogv

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07_0-5_Recap_and_Preview_2-37.ogv

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01_1-1_Basic_Indexing_7-22.ogv

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02_1-2_Index_Creation_5-40.ogv

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03_1-3_Complexity_of_Index_Creation_3-28.ogv

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04_1-4-1_Ranking_-_1_4-24.ogv

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06_1-5-1_Page_Rank_and_Memory_6-01.ogv

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08_1-6-1_Enterprise_Search_4-41.ogv

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10_1-7-1_Object_Search_5-07.ogv

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16_1-7-7_Associative_Memories_5-25.ogv

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17_1-7-8_Recap_and_Preview_2-44.ogv

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01_2-1_Preamble_-_Listen_3-18.ogv

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02_2-2_Shannon_Information_6-18.ogv

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03_2-3_Information_and_Advertising_5-49.ogv

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04_2-4_TF-IDF_8-24.ogv

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05_2-5_TF-IDF_Example_6-09.ogv

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06_2-6_Language_and_Information_8-56.ogv

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07_2-7_Machine_Learning_Intro_9-00.ogv

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08_2-8-1_Bayes_Rule_4-55.ogv

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09_2-8-2_Naive_Bayes_8-33.ogv

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10_2-9_Sentiment_Analysis_7-32.ogv

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11_2-10_Mutual_Information_8-58.ogv

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12_2-11_Machine_Learning_-_Limits_10-29.ogv

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01_3-1_Preamble_4-41.ogv

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02_3-2_Parallel_Computing_8-54.ogv

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03_3-3_Map-Reduce_11-49.ogv

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04_3-4_Map-Reduce_Example_in_Octo_11-03.ogv

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05_3-4-1_Map-Reduce_Example_in_Mincemeat_2-04.ogv

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06_3-5_Map-Reduce_Applications_13-52.ogv

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07_3-6_Parallel_Efficiency_of_Map-Reduce_8-42.ogv

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08_3-7_Inside_Map-Reduce_9-47.ogv

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01_4-0_Preamble_1-43.ogv

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02_4-1_Distributed_File_Systems_12-12.ogv

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03_4-2_Database_Technology_12-40.ogv

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04_4-3_Evolution_of_Databases_8-52.ogv

39.5M

05_4-4_Big-Table_and_HBase_10-08.ogv

54.0M

06_4-5_NoSQL_and_Eventual_Consistency_12-41.ogv

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07_4-6_Future_of_NoSQL_and_Dremel_9-28.ogv

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08_4-7_Evolution_of_SQL_and_Map-Reduce_9-34.ogv

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09_4-8_Relational_vs_Big-Data_Technologies_9-11.ogv

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10_4-9_Database_Trends_and_Summary_7-22.ogv

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01_G1_Introduction_to_Graph_Data_11-36.ogv

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02_G2_Graph_Query_Languages_14-03.ogv

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03_G3_Linked_Open_Data_12-25.ogv

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04_G4_Challenges_and_Efficiency_8-56.ogv

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05_G5_Graph_Data_Management_10-26.ogv

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06_G6_Q__A_5-03.ogv

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01_4B-1_Iteration_and_Map-Reduce_8-48.ogv

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02_4B-2_Graph_Computing_6-50.ogv

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03_4B-3_Pregel_Model_8-15.ogv

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04_4B-4_Page-rank_in_Pregel_10-35.ogv

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05_4B-5_Shortest_Paths_and_Summary_7-43.ogv

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01_5-1_Preamble_3-18.ogv

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02_5-2_Classification_Re-visited_12-56.ogv

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03_5-3_Learning_Groupings_-_Clustering_12-13.ogv

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04_5-4_Learning_Rules_10-11.ogv

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05_5-5_Association_Rule_Mining_8-45.ogv

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06_5-6_Learning_with_Big_Data_7-51.ogv

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07_5-7_Learning_Latent_Models_15-57.ogv

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08_5-8_Grounded_Learning_5-42.ogv

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09_5-9_Recap_and_Preview_2-33.ogv

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01_6-1_Preamble_9-07.ogv

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02_6-2_Logical_Inference_9-56.ogv

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03_6-3_Resolution_and_its_Limits_15-58.ogv

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04_6-4_Semantic_Web_6-18.ogv

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05_6-5_Logic_and_Uncertainty_9-09.ogv

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06_6-6_Algebra_of_Potentials_12-05.ogv

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07_6-7_Naive_Bayes_Revisited_11-26.ogv

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08_6-8-1_Bayesian_Networks_-_1_9-20.ogv

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09_6-8-2_Bayesian_Networks_-_2_5-23.ogv

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10_6-9_Information_Extraction_12-41.ogv

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11_6-10_Recap_and_Preview_7-00.ogv

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12_6-11-Programming_HW_6_4-24.ogv

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01_7-1_Preamble_2-34.ogv

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02_7-2_Linear_Prediction_11-00.ogv

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03_7-3_Least_Squares_12-08.ogv

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06_7-6_Prediction_Applications_8-30.ogv

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07_7-7_Which_Technique_6-13.ogv

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09_7-9_Hierarchical_Temporal_Memory_-_II_10-38.ogv

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10_7-10_Blackboard_Architecture_9-06.ogv

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01_M1_Motivation_11-59.ogv

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02_M2_Markov_Networks_and_Logic_8-44.ogv

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03_M3_Markov_Logic_via_an_Example_8-28.ogv

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04_M4_Markov_Logic_Formalism_11-39.ogv

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05_M5_Related_Models_10-21.ogv

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06_M6_Entity_Resolution_Example_-_1_8-37.ogv

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07_M7_Entity_Resolution_Example_-_2_9-53.ogv

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08_M8_Social_Network_Analysis_using_MLN_7-27.ogv

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09_M9_Research_Directions_in_Markov_Logic_6-09.ogv

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01_Course_Recap_and_Pointers_9-34.ogv

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SINGLE PAGE PROCESSED JP2 ZIP

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02_0-1_Revisiting_Turings_Test_3-09.srt

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03_0-2_Web-Scale_AI_and_Big_Data_3-58.srt

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05_4-4_Big-Table_and_HBase_10-08.srt

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10_4-9_Database_Trends_and_Summary_7-22.srt

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01_G1_Introduction_to_Graph_Data_11-36.srt

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04_5-4_Learning_Rules_10-11.srt

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05_5-5_Association_Rule_Mining_8-45.srt

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06_5-6_Learning_with_Big_Data_7-51.srt

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07_5-7_Learning_Latent_Models_15-57.srt

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08_5-8_Grounded_Learning_5-42.srt

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09_5-9_Recap_and_Preview_2-33.srt

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01_6-1_Preamble_9-07.srt

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02_6-2_Logical_Inference_9-56.srt

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03_6-3_Resolution_and_its_Limits_15-58.srt

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04_6-4_Semantic_Web_6-18.srt

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05_6-5_Logic_and_Uncertainty_9-09.srt

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06_6-6_Algebra_of_Potentials_12-05.srt

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05_7-5_Learning_Parameters_12-35.srt

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07_7-7_Which_Technique_6-13.srt

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10_7-10_Blackboard_Architecture_9-06.srt

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TEXT

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01_0-0_Preamble_3-26.txt

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02_0-1_Revisiting_Turings_Test_3-09.txt

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03_0-2_Web-Scale_AI_and_Big_Data_3-58.txt

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04_0-3-1_Web_Intelligence_3-22.txt

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06_0-4_Course_Outline_4-13.txt

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07_0-5_Recap_and_Preview_2-37.txt

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01_1-1_Basic_Indexing_7-22.txt

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02_1-2_Index_Creation_5-40.txt

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03_1-3_Complexity_of_Index_Creation_3-28.txt

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04_1-4-1_Ranking_-_1_4-24.txt

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14_1-7-5_LSH_Intuition_4-08.txt

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15_1-7-6_High-dimensional_Objects_8-19.txt

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03_2-3_Information_and_Advertising_5-49.txt

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06_2-6_Language_and_Information_8-56.txt

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07_2-7_Machine_Learning_Intro_9-00.txt

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12_2-11_Machine_Learning_-_Limits_10-29.txt

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04_3-4_Map-Reduce_Example_in_Octo_11-03.txt

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06_3-5_Map-Reduce_Applications_13-52.txt

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07_3-6_Parallel_Efficiency_of_Map-Reduce_8-42.txt

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08_3-7_Inside_Map-Reduce_9-47.txt

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01_4-0_Preamble_1-43.txt

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02_4-1_Distributed_File_Systems_12-12.txt

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03_4-2_Database_Technology_12-40.txt

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06_4-5_NoSQL_and_Eventual_Consistency_12-41.txt

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07_4-6_Future_of_NoSQL_and_Dremel_9-28.txt

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08_4-7_Evolution_of_SQL_and_Map-Reduce_9-34.txt

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09_4-8_Relational_vs_Big-Data_Technologies_9-11.txt

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10_4-9_Database_Trends_and_Summary_7-22.txt

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01_G1_Introduction_to_Graph_Data_11-36.txt

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02_G2_Graph_Query_Languages_14-03.txt

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03_G3_Linked_Open_Data_12-25.txt

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04_G4_Challenges_and_Efficiency_8-56.txt

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05_G5_Graph_Data_Management_10-26.txt

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06_G6_Q__A_5-03.txt

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05_4B-5_Shortest_Paths_and_Summary_7-43.txt

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02_5-2_Classification_Re-visited_12-56.txt

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03_5-3_Learning_Groupings_-_Clustering_12-13.txt

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04_5-4_Learning_Rules_10-11.txt

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05_5-5_Association_Rule_Mining_8-45.txt

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06_5-6_Learning_with_Big_Data_7-51.txt

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09_5-9_Recap_and_Preview_2-33.txt

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03_6-3_Resolution_and_its_Limits_15-58.txt

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04_6-4_Semantic_Web_6-18.txt

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05_6-5_Logic_and_Uncertainty_9-09.txt

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06_6-6_Algebra_of_Potentials_12-05.txt

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12_6-11-Programming_HW_6_4-24.txt

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04_7-4_Nonlinear_Models_9-26.txt

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05_7-5_Learning_Parameters_12-35.txt

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06_7-6_Prediction_Applications_8-30.txt

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07_7-7_Which_Technique_6-13.txt

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08_7-8_Hierarchical_Temporal_Memory_-_I_8-39.txt

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09_7-9_Hierarchical_Temporal_Memory_-_II_10-38.txt

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10_7-10_Blackboard_Architecture_9-06.txt

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01_M1_Motivation_11-59.txt

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02_M2_Markov_Networks_and_Logic_8-44.txt

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03_M3_Markov_Logic_via_an_Example_8-28.txt

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04_M4_Markov_Logic_Formalism_11-39.txt

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05_M5_Related_Models_10-21.txt

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06_M6_Entity_Resolution_Example_-_1_8-37.txt

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07_M7_Entity_Resolution_Example_-_2_9-53.txt

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08_M8_Social_Network_Analysis_using_MLN_7-27.txt

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09_M9_Research_Directions_in_Markov_Logic_6-09.txt

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01_Course_Recap_and_Pointers_9-34.txt

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[Coursera] Web Intelligence and Big Data (Indian Institute of Technology Delhi) (bigdata)

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