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Johns Hopkins Machine Learning 600.475 Fall 2014

Machine Learning 600.475 Fall 2014. Johns Hopkins Machine Learning 600.475 Fall 2014. Instructor: Prof. Mark Dredze. Office hours: Tue 3-4pm (Hackerman 226C). Poorya Mianjy: Wed 10:30-12:00pm (Malone 340). Yiming Wang: Mon 1-2:30pm (Malone 340). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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Johns Hopkins Machine Learning 600.475 Fall 2014 | cs475.org Reviews

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Machine Learning 600.475 Fall 2014. Johns Hopkins Machine Learning 600.475 Fall 2014. Instructor: Prof. Mark Dredze. Office hours: Tue 3-4pm (Hackerman 226C). Poorya Mianjy: Wed 10:30-12:00pm (Malone 340). Yiming Wang: Mon 1-2:30pm (Malone 340). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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Johns Hopkins Machine Learning 600.475 Fall 2014 | cs475.org Reviews

https://cs475.org

Machine Learning 600.475 Fall 2014. Johns Hopkins Machine Learning 600.475 Fall 2014. Instructor: Prof. Mark Dredze. Office hours: Tue 3-4pm (Hackerman 226C). Poorya Mianjy: Wed 10:30-12:00pm (Malone 340). Yiming Wang: Mon 1-2:30pm (Malone 340). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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cs475.org

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Johns Hopkins Machine Learning 600.475 Spring 2014

http://www.cs475.org/spring2014

Machine Learning 600.475 Spring 2014. Johns Hopkins Machine Learning 600.475 Spring 2014. Instructor: Prof. Mark Dredze. Office hours: Wed 3-4pm (Hackerman 226C). Purnima Rajan: Monday 4:30-6pm (NEB/Croft 227, CS undergrad lab). Yiming Wang: Thursday 2:30-4pm (Hackerman 321). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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Johns Hopkins Machine Learning 600.475 Fall 2014: Staff

http://www.cs475.org/fall2014/staff

Machine Learning 600.475 Fall 2014. WWW: www.dredze.com. For general questions, contact the instructor and the TA:. Thanks to Hosting Colombia.

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Johns Hopkins Machine Learning 600.475 Fall 2014: Syllabus

http://www.cs475.org/fall2014/syllabus

Machine Learning 600.475 Fall 2014. Overview, applications, settings. You do NOT need to read all. Of these. However, you should. Be familiar with the material. Tom Minka's nuances of probability (advanced). Wolfram Probability and stats. Construction, pruining, over-fitting. Supervised Learning: Linear Methods. Least squares and regression. Ng and Jordan, 2001. Online Learning for Structured Prediction. Perceptron, multi-class, structured. Blum On-Line Algorithms in Machine Learning. 1996. Jonathon Shle...

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Johns Hopkins Machine Learning 600.475 Fall 2014: Links

http://www.cs475.org/fall2014/links

Machine Learning 600.475 Fall 2014. Machine Learning Research at Johns Hopkins. Machine Learning Group at Johns Hopkins. Chris Bishop. Pattern Recognition and Machine Learning. 2006. Most readings will come from Bishop. You are welcome to read other books in addition to or in place of Bishop if you find them helpful. Tom Mitchell. Machine Learning. 1997. Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2009. Thanks to Hal Daume.

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Johns Hopkins Machine Learning 600.475 Fall 2012

http://www.cs475.org/fall2012

Machine Learning 600.475 Fall 2012. Johns Hopkins Machine Learning 600.475 Fall 2012. Instructor: Prof. Mark Dredze. Office hours: Mon 11am-12pm (Hackerman 226C). TA: Michael Paul and Tuo Zhao. Tuo: Tue 2-3pm (Hackerman 324). Michael: Thu 3-4pm (Hackerman 324). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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cs475.org

Johns Hopkins Machine Learning 600.475 Fall 2014

Machine Learning 600.475 Fall 2014. Johns Hopkins Machine Learning 600.475 Fall 2014. Instructor: Prof. Mark Dredze. Office hours: Tue 3-4pm (Hackerman 226C). Poorya Mianjy: Wed 10:30-12:00pm (Malone 340). Yiming Wang: Mon 1-2:30pm (Malone 340). We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods. Thanks to Hosting Colombia.

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