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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-clustering-and-retrieval
课程评论：没有评论
课程名称：机器学习：聚类与检索 课程概述：案例研究：查找相似文档 在本课程中，您将学习如何为一篇特定的新闻文章推荐相似的文章。您将探讨相似性的定义以及在文档数量众多时，如何有效检索所需的文档。课程重点是相似性算法在文档检索中的应用，同时涵盖文档的结构化表示方法，包括聚类和混合成员模型，如潜在Dirichlet分配（LDA）。您将通过期望最大化（EM）算法来学习文档聚类，并了解如何使用MapReduce扩展这些方法。 学习成果： 到课程结束时，您将能够： - 使用k最近邻创建文档检索系统。 - 识别文本数据的各种相似性度量。 - 利用KD树减少k最近邻搜索的计算量。 - 使用局部敏感哈希产生近似最近邻。 - 比较监督学习与无监督学习的任务。 - 使用k均值按主题聚类文档。 - 描述如何通过MapReduce并行化k均值。 - 检查使用混合模型的概率聚类方法。 - 使用期望最大化（EM）拟合高斯混合模型。 - 进行混合成员建模，使用潜在Dirichlet分配（LDA）。 - 描述Gibbs采样器的步骤及如何利用其输出进行推断。 - 比较非凸优化目标的初始化技术。 - 使用Python实现这些技术。 课程大纲： 1. 欢迎：介绍聚类和检索作为影响深远的机器学习工具，并提供课程的主题与背景知识概述。 2. 最近邻搜索：研究文档检索任务中的最近邻搜索，探讨数据表示及相似性度量，并实施KD树与局部敏感哈希以适应大型数据集。 3. k均值聚类：实施k均值聚类算法，发现文档的主题群体，并学习如何利用MapReduce并行化此过程。 4. 混合模型：进行基于概率模型的聚类，利用EM算法推断软分配和模型参数，并处理高维tf-idf文档表示。 5. 潜在Dirichlet分配的混合成员建模：探索LDA模型，学习如何解读输出并将其用于文档特征学习，介绍贝叶斯建模和Gibbs采样算法。 6. 层次聚类与总结：回顾课程内容，探讨其他聚类方法如层次聚类，并简要介绍未覆盖的重要聚类和检索概念。 该课程适合希望深入了解聚类和文档检索的学习者，通过理论与实践相结合的方式，提升机器学习在实际应用中的能力。
Part: 1
Title:Welcome
Description:Clustering and retrieval are some of the most high-impact machine learning tools out there. Retrieval is used in almost every applications and device we interact with, like in providing a set of products related to one a shopper is currently considering, or a list of people you might want to connect with on a social media platform. Clustering can be used to aid retrieval, but is a more broadly useful tool for automatically discovering structure in data, like uncovering groups of similar patients.
This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.
Part: 2
Title:Nearest Neighbor Search
Description:We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. We cast this problem as one of nearest neighbor search, which is a concept we have seen in the Foundations and Regression courses. However, here, you will take a deep dive into two critical components of the algorithms: the data representation and metric for measuring similarity between pairs of datapoints. You will examine the computational burden of the naive nearest neighbor search algorithm, and instead implement scalable alternatives using KD-trees for handling large datasets and locality sensitive hashing (LSH) for providing approximate nearest neighbors, even in high-dimensional spaces. You will explore all of these ideas on a Wikipedia dataset, comparing and contrasting the impact of the various choices you can make on the nearest neighbor results produced.
Part: 3
Title:Clustering with k-means
Description:In clustering, our goal is to group the datapoints in our dataset into disjoint sets. Motivated by our document analysis case study, you will use clustering to discover thematic groups of articles by "topic". These topics are not provided in this unsupervised learning task; rather, the idea is to output such cluster labels that can be post-facto associated with known topics like "Science", "World News", etc. Even without such post-facto labels, you will examine how the clustering output can provide insights into the relationships between datapoints in the dataset. The first clustering algorithm you will implement is k-means, which is the most widely used clustering algorithm out there. To scale up k-means, you will learn about the general MapReduce framework for parallelizing and distributing computations, and then how the iterates of k-means can utilize this framework. You will show that k-means can provide an interpretable grouping of Wikipedia articles when appropriately tuned.
Part: 4
Title:Mixture Models
Description:In k-means, observations are each hard-assigned to a single cluster, and these assignments are based just on the cluster centers, rather than also incorporating shape information. In our second module on clustering, you will perform probabilistic model-based clustering that provides (1) a more descriptive notion of a "cluster" and (2) accounts for uncertainty in assignments of datapoints to clusters via "soft assignments". You will explore and implement a broadly useful algorithm called expectation maximization (EM) for inferring these soft assignments, as well as the model parameters. To gain intuition, you will first consider a visually appealing image clustering task. You will then cluster Wikipedia articles, handling the high-dimensionality of the tf-idf document representation considered.
Part: 5
Title:Mixed Membership Modeling via Latent Dirichlet Allocation
Description:The clustering model inherently assumes that data divide into disjoint sets, e.g., documents by topic. But, often our data objects are better described via memberships in a collection of sets, e.g., multiple topics. In our fourth module, you will explore latent Dirichlet allocation (LDA) as an example of such a mixed membership model particularly useful in document analysis. You will interpret the output of LDA, and various ways the output can be utilized, like as a set of learned document features. The mixed membership modeling ideas you learn about through LDA for document analysis carry over to many other interesting models and applications, like social network models where people have multiple affiliations.
Throughout this module, we introduce aspects of Bayesian modeling and a Bayesian inference algorithm called Gibbs sampling. You will be able to implement a Gibbs sampler for LDA by the end of the module.
Part: 6
Title:Hierarchical Clustering & Closing Remarks
Description:In the conclusion of the course, we will recap what we have covered. This represents both techniques specific to clustering and retrieval, as well as foundational machine learning concepts that are more broadly useful.
We provide a quick tour into an alternative clustering approach called hierarchical clustering, which you will experiment with on the Wikipedia dataset. Following this exploration, we discuss how clustering-type ideas can be applied in other areas like segmenting time series. We then briefly outline some important clustering and retrieval ideas that we did not cover in this course.
We conclude with an overview of what's in store for you in the rest of the specialization.
Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.
