01:03
IR20.11 Summary
02:32
IR20.8 Learning to rank with an SVM
05:41
IR20.10 Learning to rank with click data
01:11
IR20.9 Learning to rank: features
04:24
IR20.7 Learning to rank for Information Retrieval
13:16
IR20.3 Passive-aggressive algorithm (PA)
10:01
IR20.5 SVM explained visually
06:01
IR20.2 Large margin classification
06:16
IR20.1 Centroid classifier
06:47
IR20.4 Convergence of the PA algorithm
08:54
IR20.6 Sequential minimal optimization (SMO)
01:07
LM.9 Jelinek-Mercer smoothing
11:03
LM.7 Good-Turing estimate
01:45
LM.4 The unigram model (urn model)
02:39
LM.14 Issues to consider
02:36
LM.8 Interpolation with background model
02:07
LM.2 What is a language model?
02:21
LM.10 Dirichlet smoothing
00:54
LM.13 Language model ranking formula
02:30
LM.11 Leave-one-out smoothing
02:03
LM.5 Zero-frequency problem
05:03
LM.3 Query likelihood ranking
02:17
LM.1 Overview
02:27
LM.6 Laplace correction and absolute discounting
08:07
LM.12 Smoothing and inverse document frequency
02:49
BIR.10 Estimation with relevant examples
03:12
BIR.17 Modelling term frequency
14:00
BIR.16 Linked dependence assumption
03:52
BIR.12 Example
02:14
BIR.3 Probability of relevance
04:55
BIR.9 Estimating class models
06:43
BIR.14 van Rijsbergen's tree dependence model
01:43
BIR.13 Summary of assumptions
00:54
BIR.5 From PRP to a retrieval model
01:03
BIR.20 Summary
08:25
BIR.11 Estimation without examples
06:07
BIR.8 Natural zero
04:33
BIR.1 Formal models in Information Retrieval
03:20
BIR.2 Probability ranking principle
03:43
BIR.15 Term dependence models in IR
00:44
BIR.19 The Okapi BM25 formula
04:43
BIR.18 Harter's two-Poisson model
03:25
BIR.7 Word independence assumption
06:01
BIR.6 Ranking by the odds ratio
09:48
BIR.4 Probability ranking is optimal
04:27
LSH.6 Error rates for exact duplicate detection
09:17
LSH.11 Hash-code length and number of hashtables
03:01
LSH.3 Detecting duplicates by hashing
10:42
LSH.10 False positive and negative errors of LSH
04:42
LSH.12 Simhash algorithm
01:58
LSH.5 Clarification
04:15
LSH.8 Locality-sensitive hashing: the idea
00:39
LSH.13 Clarification
08:35
LSH.4 Adler32 hashcode
16:18
LSH.9 Locality-sensitive hashing: how it works
01:12
LSH.2 Duplicate detection: naive approach
01:53
LSH.7 Properties of conventional hashcodes
02:16
LSH.1 Exact duplicates and near-duplicates
02:32
IR11.4 Content and the DOM tree
04:20
IR11.3 Extracting content from HTML
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