课程信息

36,676 次近期查看

学生职业成果

37%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利

100% 在线

立即开始,按照自己的计划学习。

第 4 门课程(共 4 门)

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为46 小时

建议:6 weeks of study, 5-8 hours/week...

英语(English)

字幕:英语(English), 韩语, 阿拉伯语(Arabic)

您将获得的技能

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree

学生职业成果

37%

完成这些课程后已开始新的职业生涯

38%

通过此课程获得实实在在的工作福利

100% 在线

立即开始,按照自己的计划学习。

第 4 门课程(共 4 门)

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为46 小时

建议:6 weeks of study, 5-8 hours/week...

英语(English)

字幕:英语(English), 韩语, 阿拉伯语(Arabic)

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 1 小时

Welcome

完成时间为 1 小时
4 个视频 (总计 25 分钟), 4 个阅读材料
4 个视频
Course overview3分钟
Module-by-module topics covered8分钟
Assumed background6分钟
4 个阅读材料
Important Update regarding the Machine Learning Specialization10分钟
Slides presented in this module10分钟
Software tools you'll need for this course10分钟
A big week ahead!10分钟
2

2

完成时间为 4 小时

Nearest Neighbor Search

完成时间为 4 小时
22 个视频 (总计 137 分钟), 4 个阅读材料, 5 个测验
22 个视频
1-NN algorithm2分钟
k-NN algorithm6分钟
Document representation5分钟
Distance metrics: Euclidean and scaled Euclidean6分钟
Writing (scaled) Euclidean distance using (weighted) inner products4分钟
Distance metrics: Cosine similarity9分钟
To normalize or not and other distance considerations6分钟
Complexity of brute force search1分钟
KD-tree representation9分钟
NN search with KD-trees7分钟
Complexity of NN search with KD-trees5分钟
Visualizing scaling behavior of KD-trees4分钟
Approximate k-NN search using KD-trees7分钟
Limitations of KD-trees3分钟
LSH as an alternative to KD-trees4分钟
Using random lines to partition points5分钟
Defining more bins3分钟
Searching neighboring bins8分钟
LSH in higher dimensions4分钟
(OPTIONAL) Improving efficiency through multiple tables22分钟
A brief recap2分钟
4 个阅读材料
Slides presented in this module10分钟
Choosing features and metrics for nearest neighbor search10分钟
(OPTIONAL) A worked-out example for KD-trees10分钟
Implementing Locality Sensitive Hashing from scratch10分钟
5 个练习
Representations and metrics12分钟
Choosing features and metrics for nearest neighbor search10分钟
KD-trees10分钟
Locality Sensitive Hashing10分钟
Implementing Locality Sensitive Hashing from scratch10分钟
3

3

完成时间为 2 小时

Clustering with k-means

完成时间为 2 小时
13 个视频 (总计 79 分钟), 2 个阅读材料, 3 个测验
13 个视频
An unsupervised task6分钟
Hope for unsupervised learning, and some challenge cases4分钟
The k-means algorithm7分钟
k-means as coordinate descent6分钟
Smart initialization via k-means++4分钟
Assessing the quality and choosing the number of clusters9分钟
Motivating MapReduce8分钟
The general MapReduce abstraction5分钟
MapReduce execution overview and combiners6分钟
MapReduce for k-means7分钟
Other applications of clustering7分钟
A brief recap1分钟
2 个阅读材料
Slides presented in this module10分钟
Clustering text data with k-means10分钟
3 个练习
k-means18分钟
Clustering text data with K-means16分钟
MapReduce for k-means10分钟
4

4

完成时间为 3 小时

Mixture Models

完成时间为 3 小时
15 个视频 (总计 91 分钟), 4 个阅读材料, 3 个测验
15 个视频
Aggregating over unknown classes in an image dataset6分钟
Univariate Gaussian distributions2分钟
Bivariate and multivariate Gaussians7分钟
Mixture of Gaussians6分钟
Interpreting the mixture of Gaussian terms5分钟
Scaling mixtures of Gaussians for document clustering5分钟
Computing soft assignments from known cluster parameters7分钟
(OPTIONAL) Responsibilities as Bayes' rule5分钟
Estimating cluster parameters from known cluster assignments6分钟
Estimating cluster parameters from soft assignments8分钟
EM iterates in equations and pictures6分钟
Convergence, initialization, and overfitting of EM9分钟
Relationship to k-means3分钟
A brief recap1分钟
4 个阅读材料
Slides presented in this module10分钟
(OPTIONAL) A worked-out example for EM10分钟
Implementing EM for Gaussian mixtures10分钟
Clustering text data with Gaussian mixtures10分钟
3 个练习
EM for Gaussian mixtures18分钟
Implementing EM for Gaussian mixtures12分钟
Clustering text data with Gaussian mixtures8分钟

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关于 ?????

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

关于 ???? 专项课程

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
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