Unsupervised Algorithms in Machine Learning

所在平台: Coursera

课程主页: https://www.coursera.org/learn/unsupervised-algorithms-in-machine-learning

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课程简介

课程名称：无监督学习算法 概述：本课程深入探讨从未标记数据中发现隐藏模式的重要性，旨在为您的数据科学工具包增加这一需求日益增长的技能。我们将学习选定的无监督学习方法，包括降维、聚类和潜在特征学习，重点介绍基于真实世界案例的应用，例如推荐系统，并结合产品推荐算法的实例进行实践操作。 先决条件：参与者需要具备编码或脚本编写的基本知识，并将在课程中广泛使用Python。同时，学员需具备大学级别的数学技能，包括微积分和线性代数。虽然推荐在本课程之前学习"有监督学习概论"课程，但并不是必须的。 此课程可以作为科罗拉多大学博尔德分校 (CU Boulder) 数据科学硕士学位 (MS-DS) 项目的一部分进行学术学分学习。该硕士学位是一个跨学科的项目，结合了应用数学、计算机科学、信息科学等多个学科的师资力量。该项目采用基于表现的招生方式，无需申请，适合拥有广泛本科教育背景和/或计算机科学、信息科学、数学及统计方面专业经验的个人。了解更多关于MS-DS项目的信息，请访问：https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 第一部分：无监督学习简介 我们将从无监督学习入手，探索如何从未标记的数据中发现潜在结构。讲授PCA（主成分分析），作为一种基础的降维技术，帮助提升分类和回归任务的效果。 第二部分：聚类 本周将重点关注聚类，这是最流行的无监督学习方法之一。我们将学习K-means聚类和层次聚类，分析其优缺点以及选择距离或连接度量标准。 第三部分：推荐系统 本周将探讨推荐系统，包括Netflix、亚马逊和YouTube等网站如何为用户提供个性化推荐。我们会考虑不同的推荐策略，如基于流行度、内容和协同过滤方法，并讨论在计算时间和稀疏数据等方面的挑战。 第四部分：矩阵分解 在课程的最后一周，我们将学习潜在因子模型，包括受欢迎的矩阵分解（MF）技术，探讨其在推荐系统、图像分析、文本挖掘等领域的多种应用。同时，学生将参与Kaggle迷你项目，使用矩阵分解对新闻文章进行分类。 加入这一课程，无论你是希望提升自己的数据科学技能，还是希望为未来的学业打下良好基础，这都是一个理想的选择。

课程大纲

Part: 1

Title:Unsupervised Learning Intro

Description:Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning. In this course, the models no longer have labels to learn from. They need to make sense of the data from the observations themselves. This week we are diving into Principal Component Analysis, PCA, a foundational dimension reduction technique. When you first start learning this topic, it might not seem easy. There is undoubtedly some math involved in this section. However, PCA can be grasped conceptually, perhaps more readily than anticipated. In the Supervised Learning course, we struggled with the Curse of Dimensionality. This week, we will see how PCA can reduce the number of dimensions and improve classification/regression tasks. You will have reading, a quiz, and a Jupyter notebook lab/Peer Review to implement the PCA algorithm.

Part: 2

Title:Clustering

Description:This week, we are working with clustering, one of the most popular unsupervised learning methods. Last week, we used PCA to find a low-dimensional representation of data. Clustering, on the other hand, finds subgroups among observations. We can get a meaningful intuition of the data structure or use a procedure like Cluster-then-predict. Clustering has several applications ranging from marketing customer segmentation and advertising, identifying similar movies/music, to genomics research and disease subtypes discovery. We will focus our efforts mainly on K-means clustering and hierarchical clustering with consideration to the benefits and disadvantages of both and the choice of metrics like distance or linkage. We have reading, a quiz, and a Jupyter notebook lab/Peer Review this week.

Part: 3

Title:Recommender System

Description:This week we are working with Recommender Systems. Websites like Netflix, Amazon, and YouTube will surface personalized recommendations for movies, items, or videos. This week, we explore Recommendation Engines' strategies to predict users' likes. We will consider popularity, content-based, and collaborative filtering approaches, and what similarity metrics to use. As we work with Recommendation Systems, there are challenges, like the time complexity of operations and sparse data. This week is relatively math dense. You will have a quiz wherein you will work with different similarity metric calculations. Give yourself time for this week's Jupyter notebook lab and consider performant implementations. The Peer Review section this week is short.

Part: 4

Title:Matrix Factorization

Description:We are already at the last week of course material! Get ready for another dense math week. Last week, we learned about Recommendation Systems. We used a Neighborhood Method of Collaborative Filtering, utilizing similarity measures. Latent Factor Models, including the popular Matrix Factorization (MF), can also be used for Collaborative Filtering. A 1999 publication in Nature made Non-negative Matrix Factorization extremely popular. MF has many applications, including image analysis, text mining/topic modeling, Recommender systems, audio signal separation, analytic chemistry, and gene expression analysis. For this week, we focus on Singular Value Decomposition, Non-negative Matrix Factorization, and Approximation methods. This week, we have reading, a quiz, and a Kaggle mini-project utilizing matrix factorization to categorize news articles.

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