Wang, Shaojun

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Wright State University CORE Scholar Computer Science & Engineering Syllabi College of Engineering & Computer Science Winter 2008 CS 714-01: Machine Learning Shaojun Wang Wright State University - Main Campus, shaojun.wang@wright.edu Follow this and additional works at: https://corescholar.libraries.wright.edu/cecs_syllabi  Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Wang, S. (2008). CS 714-01: Machine Learning. . https://corescholar.libraries.wright.edu/cecs_syllabi/730 This Syllabus is brought to you for free and open access by the College of Engineering & Computer Science at CORE Scholar. It has been accepted for inclusion in Computer Science & Engineering Syllabi by an authorized administrator of CORE Scholar. For more information, please contact library-corescholar@wright.edu. Page 1 of2 CS714: MACHINE LEARNING Day 1/07/08 1/09/08 1/14/08 Ill 6/08 1/21108 1/23/08 J/28/08 1/30/08 2/04/08 2/06/08 2111/08 2/13/08 2/18/08 2/20/08 2/25/08 Topic Introduction Linear prediction Generalized linear prediction Regularization, neural networks Learning theory: Bias-variance Automated complexity control Linear classification, suppmt vector machines Duality Kernels Multiclass prediction Learning theory: Uniform convergence Vapnik-Chervonenkis dimension Combining classifiers, boosting Probability models Bayesian networks and WINTER2008 INFORMATION 8-\'I.~AIUIS ASSJS,;~Jc~l.EJ~TS Reading B 1-2; HTF 1 Optional Reading Th~~gL~<!ipline of1IH!QQlI!~ l~;:}rning by T. Mitchell 83.1.1-2 B; HTF 5.1­2, 5.7, 5.9, 6.1-3 B 3.1, 5; RTF 3.4, 5.4, ll;BS B 3.2; HTF 2.9, 7 HTF7 B 7.1.1-2; HTF 4.5, 12.1-3 HTF 4.5, 12.1-3 EKQ~tP1 from Vapnik's The Natural ofStatistical Learning Themy; £l1J2PQtl vectors machines with ~_p1~11tions HTF 5.8, 6.1-2,6.7 B 7.1.3 HTF 7.9 HTFJO Toy example, training error p_roofand ~lid~~by Schapire B 8.1, 8.3 http://www.cs.wright.edu/-swang/cs7 l 4/syllabus.html 1114/2008 Page 2 of2 Markov random fields 2127108 	 Maximum likelihood B estimation 3/03/08 	 Expectation- B 9.2-4; Maximization algorithm HTF 8.5 3105108 Hidden Markov models B 13.1-2 	 _HMM tutQ_rial by L. Rabiner; Exarri_ples 3il0/08 	 Structured prediction: ~RFJlliP~r and video by conditional random fields Lafferty et al 3/12/08 	 Structured prediction: M3NJ!f!P~I and fili4~~ by max-margin Markov Taskar et al; M~NJyt,9ri<ll networks by S. Lacoste-Julien 3/17/08 Challenges in statistical Clrn,Jleng_es in2tfills1i~al machine learning machine leaifling and yjde9 by J. Lafferty 1114/2008http://www.cs.wright.edu/-swang/cs7 l 4/syllabus.html 

CS 714-01: Machine Learning

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CS 714-01: Machine Learning

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