Machine Learning: Unsupervised Learning

课程
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来源
  • 来自www.udacity.com
状况
  • 自定进度
  • 免费获取
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  • 4 序列
  • 等级 介绍

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课程详情

教学大纲

Lesson 1: Randomized optimization

- Optimization, randomized- Hill climbing- Random restart hill climbing- Simulated annealing- Annealing algorithm- Properties of simulated annealing- Genetic algorithms- GA skeleton- Crossover example- What have we learned- MIMIC- MIMIC: A probability model- MIMIC: Pseudo code- MIMIC: Estimating distributions- Finding dependency trees- Probability distribution

Lesson 2: Clustering

- Clustering and expectation maximization- Basic clustering problem- Single linkage clustering (SLC)- Running time of SLC- Issues with SLC- K-means clustering- K-means in Euclidean space- K-means as optimization- Soft clustering- Maximum likelihood Gaussian- Expectation Maximization (EM)- Impossibility theorem

Lesson 3: Feature Selection

- Algorithms- Filtering and Wrapping- Speed- Searching- Relevance- Relevance vs. Usefulness

Lesson 4: Feature Transformation

- Feature Transformation- Words like Tesla- Principal Components Analysis- Independent Components Analysis- Cocktail Party Problem- Matrix- Alternatives

Lesson 5: Information Theory

- History-Sending a Message- Expected size of the message- Information between two variables- Mutual information- Two Independent Coins- Two Dependent Coins- Kullback Leibler Divergence###Unsupervised Learning Project

先决条件

没有。

讲师

  • Charles Isbell - Charles Isbell is a Professor and Senior Associate Dean at the School of Interactive Computing at Georgia Tech. His research passion is artificial intelligence, particularly on building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Lately, he has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition), scalable coordination, and development environments that support the rapid prototyping of adaptive agents. He is developing adaptive programming languages, and trying to understand what it means to bring machine learning tools to non-expert authors, designers and developers. He sometimes interacts with the physical world through racquetball, weight-lifting and Ultimate Frisbee.
  • Michael Littman - Michael Littman is a Professor of Computer Science at Brown University. He also teaches Udacity’s Algorithms course (CS215) on crunching social networks. Prior to joining Brown in 2012, he led the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3) at Rutgers, where he served as the Computer Science Department Chair from 2009-2012. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), served as program chair for AAAI's 2013 conference and the International Conference on Machine Learning in 2009, and received university-level teaching awards at both Duke and Rutgers. Charles Isbell taught him about racquetball, weight-lifting and Ultimate Frisbee, but he's not that great at any of them. He's pretty good at singing and juggling, though.

编辑

佐治亚理工学院(Georgia Institute of Technology),又称佐治亚理工学院或 GT,是一所位于美国佐治亚州亚特兰大市的男女同校公立研究型大学。它是佐治亚大学系统网络的一部分。佐治亚理工学院在萨凡纳(美国佐治亚州)、梅斯(法国)、阿斯隆(爱尔兰)、上海(中国)和新加坡设有办事处。

佐治亚理工学院的工程和计算机科学课程在世界上名列前茅5,6 ,声誉卓著。此外,佐治亚理工学院还开设了科学、建筑、人文和管理课程。

平台

Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant cours en ligne ouvert et massif.

Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.

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