Dan’s Course on Machine Learning at Illinois CS446

Useful if you want to refresh your understanding of a first course in ML.

Recommended Books

The class will be based mostly on papers that will be made available. The books are mentioned only for background reading.

NLP

• Daniel Jurafsky and James H. Martin, Speech and Language Processing , 2nd Edition, 2008
• Christopher D. Manning and Hinrich Schutze, Foundations of Statistical Natural Language Processing , MIT Press 1999
• Eugene Charniak, Statistical Language Learning, MIT Press 1993
• Frederick Jelinek, Statistical Methods for Speech Recognition, MIT Press 1998
• Steve Young and G. Bloothooft (Eds), Corpus-Based Methods in Language and Speech Processing, Kluwer
• James Allen, Natural Language Understanding, Addison-Wesley
• Linguistic Structure Prediction by Noah A. Smith. Electronic Version available here.
• Natural Language Processing papers at the ACL anthology.

ML

• Chapters 4,17,18 from A Course in Machine Learning by Hal Daumé III
• Stephen Boyd and Lieven Vandenberghe, Convex Optimization, 2004. Essential for understanding loss minimization and inference algorithms like dual decomposition.
• Proceedings of machine learning and learning theory papers at JMLR: Workshop and Conference Proceedings.

Related Courses

• CMU Structured Prediction Course in Fall 2011
• Cornell Structured Prediction Course in Fall 2015
• Univ of Utah Structured Prediction Course in Spring 2017 - closely follows our course.

Recent Tutorials on Structured Prediction in NLP

• V Srikumar and D Roth. Integer Linear Programming Formulations in Natural Language Processing EACL 2017.
• K-W Chang, V Srikumar, G Kundu and D Roth Learning and Inference in Structured Prediction Models AAAI 2016.
• AFT Martin. Linear Programming Decoders in NLP EMNLP 2014.
• N Smith. Structured Prediction for Natural Language Processing ICML 2009.

Others

• How To Read a Paper
• How NOT To Review a Paper