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강의 계획 - 이 강좌에서 배울 내용

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1

1

완료하는 데 1시간 필요

Welcome

완료하는 데 1시간 필요
4개 동영상 (총 25분), 4 개의 읽기 자료
4개의 동영상
Course overview3m
Module-by-module topics covered8m
Assumed background6m
4개의 읽기 자료
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Software tools you'll need for this course10m
A big week ahead!10m
2

2

완료하는 데 4시간 필요

Nearest Neighbor Search

완료하는 데 4시간 필요
22개 동영상 (총 137분), 4 개의 읽기 자료, 5 개의 테스트
22개의 동영상
1-NN algorithm2m
k-NN algorithm6m
Document representation5m
Distance metrics: Euclidean and scaled Euclidean6m
Writing (scaled) Euclidean distance using (weighted) inner products4m
Distance metrics: Cosine similarity9m
To normalize or not and other distance considerations6m
Complexity of brute force search1m
KD-tree representation9m
NN search with KD-trees7m
Complexity of NN search with KD-trees5m
Visualizing scaling behavior of KD-trees4m
Approximate k-NN search using KD-trees7m
Limitations of KD-trees3m
LSH as an alternative to KD-trees4m
Using random lines to partition points5m
Defining more bins3m
Searching neighboring bins8m
LSH in higher dimensions4m
(OPTIONAL) Improving efficiency through multiple tables22m
A brief recap2m
4개의 읽기 자료
Slides presented in this module10m
Choosing features and metrics for nearest neighbor search10m
(OPTIONAL) A worked-out example for KD-trees10m
Implementing Locality Sensitive Hashing from scratch10m
5개 연습문제
Representations and metrics12m
Choosing features and metrics for nearest neighbor search10m
KD-trees10m
Locality Sensitive Hashing10m
Implementing Locality Sensitive Hashing from scratch10m
3

3

완료하는 데 2시간 필요

Clustering with k-means

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13개 동영상 (총 79분), 2 개의 읽기 자료, 3 개의 테스트
13개의 동영상
An unsupervised task6m
Hope for unsupervised learning, and some challenge cases4m
The k-means algorithm7m
k-means as coordinate descent6m
Smart initialization via k-means++4m
Assessing the quality and choosing the number of clusters9m
Motivating MapReduce8m
The general MapReduce abstraction5m
MapReduce execution overview and combiners6m
MapReduce for k-means7m
Other applications of clustering7m
A brief recap1m
2개의 읽기 자료
Slides presented in this module10m
Clustering text data with k-means10m
3개 연습문제
k-means18m
Clustering text data with K-means16m
MapReduce for k-means10m
4

4

완료하는 데 3시간 필요

Mixture Models

완료하는 데 3시간 필요
15개 동영상 (총 91분), 4 개의 읽기 자료, 3 개의 테스트
15개의 동영상
Aggregating over unknown classes in an image dataset6m
Univariate Gaussian distributions2m
Bivariate and multivariate Gaussians7m
Mixture of Gaussians6m
Interpreting the mixture of Gaussian terms5m
Scaling mixtures of Gaussians for document clustering5m
Computing soft assignments from known cluster parameters7m
(OPTIONAL) Responsibilities as Bayes' rule5m
Estimating cluster parameters from known cluster assignments6m
Estimating cluster parameters from soft assignments8m
EM iterates in equations and pictures6m
Convergence, initialization, and overfitting of EM9m
Relationship to k-means3m
A brief recap1m
4개의 읽기 자료
Slides presented in this module10m
(OPTIONAL) A worked-out example for EM10m
Implementing EM for Gaussian mixtures10m
Clustering text data with Gaussian mixtures10m
3개 연습문제
EM for Gaussian mixtures18m
Implementing EM for Gaussian mixtures12m
Clustering text data with Gaussian mixtures8m

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MACHINE LEARNING: CLUSTERING & RETRIEVAL의 최상위 리뷰

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기계 학습 특화 과정 정보

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....
기계 학습

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