UW Machine Learning: Clustering & Retrieval 华盛顿大学 机器学习 聚类检索(无字幕)

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2017-10-14 15:39:37
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URL: https://www.coursera.org/learn/ml-clustering-and-retrieval Week 1: 1 - 4 Week 2: 5 - 26 Week 3: 27 - 39 Week 4: Week 5: Week 6: My Github: https://github.com/SSQ/Coursera-UW-Machine-Learning-Clustering-Retrieval
machine learning
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Welcome and introduction to clustering and retrieval tasks
06:24
Course overview
03:29
Module-by-module topics covered
08:52
Assumed background
06:12
retrieval as k-nearest neighbor search
02:50
1-NN algorithm
02:55
k-NN algorithm
06:49
Document representation
05:53
Distance metrics Euclidean and scaled Euclidean
06:42
Writing (scaled) Euclidean distance using (weighted) inner products
04:02
Distance metrics Cosine similarity
09:01
To normalize or not and other distance considerations
06:59
Complexity of brute force search
01:51
KD-tree representation
09:38
NN search with KD-trees
07:09
Complexity of NN search with KD-trees
05:39
Visualizing scaling behavior of KD-trees
04:24
Approximate k-NN search using KD-trees
07:46
Limitations of KD-trees
03:33
LSH as an alternative to KD-trees
04:20
Using random lines to partition points
05:41
Defining more bins
03:29
Searching neighboring bins
08:38
LSH in higher dimensions
04:09
(OPTIONAL) Improving efficiency through multiple tables
22:47
A brief recap
02:30
The goal of clustering
03:27
An unsupervised task
06:42
Hope for unsupervised learning, and some challenge cases
04:09
The k-means algorithm
07:47
k-means as coordinate descent
06:01
Smart initialization via k-means++
04:49
Assessing the quality and choosing the number of clusters
09:27
Motivating MapReduce
08:48
The general MapReduce abstraction
05:16
MapReduce execution overview and combiners
06:20
MapReduce for k-means
07:22
Other applications of clustering
07:15
A brief recap
01:28
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