課程信息
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第 4 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

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完成時間大約為47 小時

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

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)
User
學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Systems Analysts
  • Data Analysts

您將獲得的技能

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree
User
學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Systems Analysts
  • Data Analysts

第 4 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為47 小時

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

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

Welcome

4 個視頻 (總計 25 分鐘), 4 個閱讀材料
4 個視頻
Course overview3分鐘
Module-by-module topics covered8分鐘
Assumed background6分鐘
4 個閱讀材料
Important Update regarding the Machine Learning Specialization10分鐘
Slides presented in this module10分鐘
Software tools you'll need for this course10分鐘
A big week ahead!10分鐘
2
完成時間為 4 小時

Nearest Neighbor Search

22 個視頻 (總計 137 分鐘), 4 個閱讀材料, 5 個測驗
22 個視頻
1-NN algorithm2分鐘
k-NN algorithm6分鐘
Document representation5分鐘
Distance metrics: Euclidean and scaled Euclidean6分鐘
Writing (scaled) Euclidean distance using (weighted) inner products4分鐘
Distance metrics: Cosine similarity9分鐘
To normalize or not and other distance considerations6分鐘
Complexity of brute force search1分鐘
KD-tree representation9分鐘
NN search with KD-trees7分鐘
Complexity of NN search with KD-trees5分鐘
Visualizing scaling behavior of KD-trees4分鐘
Approximate k-NN search using KD-trees7分鐘
Limitations of KD-trees3分鐘
LSH as an alternative to KD-trees4分鐘
Using random lines to partition points5分鐘
Defining more bins3分鐘
Searching neighboring bins8分鐘
LSH in higher dimensions4分鐘
(OPTIONAL) Improving efficiency through multiple tables22分鐘
A brief recap2分鐘
4 個閱讀材料
Slides presented in this module10分鐘
Choosing features and metrics for nearest neighbor search10分鐘
(OPTIONAL) A worked-out example for KD-trees10分鐘
Implementing Locality Sensitive Hashing from scratch10分鐘
5 個練習
Representations and metrics12分鐘
Choosing features and metrics for nearest neighbor search10分鐘
KD-trees10分鐘
Locality Sensitive Hashing10分鐘
Implementing Locality Sensitive Hashing from scratch10分鐘
3
完成時間為 2 小時

Clustering with k-means

13 個視頻 (總計 79 分鐘), 2 個閱讀材料, 3 個測驗
13 個視頻
An unsupervised task6分鐘
Hope for unsupervised learning, and some challenge cases4分鐘
The k-means algorithm7分鐘
k-means as coordinate descent6分鐘
Smart initialization via k-means++4分鐘
Assessing the quality and choosing the number of clusters9分鐘
Motivating MapReduce8分鐘
The general MapReduce abstraction5分鐘
MapReduce execution overview and combiners6分鐘
MapReduce for k-means7分鐘
Other applications of clustering7分鐘
A brief recap1分鐘
2 個閱讀材料
Slides presented in this module10分鐘
Clustering text data with k-means10分鐘
3 個練習
k-means18分鐘
Clustering text data with K-means16分鐘
MapReduce for k-means10分鐘
4
完成時間為 3 小時

Mixture Models

15 個視頻 (總計 91 分鐘), 4 個閱讀材料, 3 個測驗
15 個視頻
Aggregating over unknown classes in an image dataset6分鐘
Univariate Gaussian distributions2分鐘
Bivariate and multivariate Gaussians7分鐘
Mixture of Gaussians6分鐘
Interpreting the mixture of Gaussian terms5分鐘
Scaling mixtures of Gaussians for document clustering5分鐘
Computing soft assignments from known cluster parameters7分鐘
(OPTIONAL) Responsibilities as Bayes' rule5分鐘
Estimating cluster parameters from known cluster assignments6分鐘
Estimating cluster parameters from soft assignments8分鐘
EM iterates in equations and pictures6分鐘
Convergence, initialization, and overfitting of EM9分鐘
Relationship to k-means3分鐘
A brief recap1分鐘
4 個閱讀材料
Slides presented in this module10分鐘
(OPTIONAL) A worked-out example for EM10分鐘
Implementing EM for Gaussian mixtures10分鐘
Clustering text data with Gaussian mixtures10分鐘
3 個練習
EM for Gaussian mixtures18分鐘
Implementing EM for Gaussian mixtures12分鐘
Clustering text data with Gaussian mixtures8分鐘
4.6
305 個審閱Chevron Right

37%

完成這些課程後已開始新的職業生涯

38%

通過此課程獲得實實在在的工作福利

來自Machine Learning: Clustering & Retrieval的熱門評論

創建者 BKAug 25th 2016

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.

創建者 JMJan 17th 2017

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.

講師

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Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

關於 华盛顿大学

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....

關於 机器学习 專項課程

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....
机器学习

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