list 4 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

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En résumé

*This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.* Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning! Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches -- including randomized optimization, clustering, and feature selection and transformation -- to find structure in unlabeled data. **Series Information**: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. - [Machine Learning 1: Supervised Learning](https://www.udacity.com/course/ud675) - [Machine Learning 2: Unsupervised Learning](https://www.udacity.com/course/ud741) (this course) - [Machine Learning 3: Reinforcement Learning](https://www.udacity.com/course/ud820) If you are new to Machine Learning, we suggest you take these 3 courses in order. The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

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Le programme

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
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Les intervenants

  • 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.
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Le concepteur

The Georgia Institute of Technology is one of the nation's top research universities, distinguished by its commitment to improving the human condition through advanced science and technology. Georgia Tech's campus occupies 400 acres in the heart of the city of Atlanta, where more than 20,000 undergraduate and graduate students receive a focused, technologically based education.
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La plateforme

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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