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Fundamentals of Machine Learning最新文献

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Reinforcement learning 强化学习
Fundamentals of Machine Learning Pub Date : 2019-11-28 DOI: 10.1093/oso/9780198828044.003.0010
T. Trappenberg
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引用次数: 0
Generative models 生成模型
Fundamentals of Machine Learning Pub Date : 2019-11-28 DOI: 10.1093/oso/9780198828044.003.0008
T. Trappenberg
{"title":"Generative models","authors":"T. Trappenberg","doi":"10.1093/oso/9780198828044.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780198828044.003.0008","url":null,"abstract":"This chapter presents an introduction to the important topic of building generative models. These are models that are aimed to understand the variety of a class such as cars or trees. A generative mode should be able to generate feature vectors for instances of the class they represent, and such models should therefore be able to characterize the class with all its variations. The subject is discussed both in a Bayesian and in a deep learning context, and also within a supervised and unsupervised context. This area is related to important algorithms such as k-means clustering, expectation maximization (EM), naïve Bayes, generative adversarial networks (GANs), and variational autoencoders (VAE).","PeriodicalId":300420,"journal":{"name":"Fundamentals of Machine Learning","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127900018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Basic probability theory 基本概率论
Fundamentals of Machine Learning Pub Date : 2019-11-28 DOI: 10.1093/oso/9780198828044.003.0006
T. Trappenberg
{"title":"Basic probability theory","authors":"T. Trappenberg","doi":"10.1093/oso/9780198828044.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780198828044.003.0006","url":null,"abstract":"The discussion provides a refresher of probability theory, in particular with respect to the formulations that build the theoretical language of modern machine learning. Probability theory is the formalism of random numbers, and this chapter outlines what these are and how they are characterized by probability density or probability mass functions. How such functions have traditionally been characterized is covered, and a review of how to work with such mathematical objects such as transforming density functions and how to measure differences between density function is presented. Definitions and basic operations with multiple random variables, including the Bayes law, are covered. The chapter ends with an outline of some important approximation techniques of so-called Monte Carlo methods.","PeriodicalId":300420,"journal":{"name":"Fundamentals of Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128918179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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