Natural Language Processing and

#	Slides	Reading and Teaching Materials
1	General Introduction of Computing and NLP	Reference Books Foundations of Statistical Natural Language Processing (Manning and Schutze) (Internal Use Only) Machine Learning: the Art and the Science (Peter Flach) (Internal Use Only) Machine Learning: A Probabilistic Perspective (Kevin Murphy) (Internal Use Only) Contribution of Alan Turing, an article from Nature (Nature, 492, 2012)
2	Mathematical Foundations for NLP Language Models	Lecture Note The book: Introduction to Probability from MIT Readings 1: Zipf's Law in Chinese, Zipf's Law in City Size Readings 2: Entropy of English, Entropy of Chinese
3	Noisy Channel Model Hidden Markov Model	Lecture Note Hall of Fame of A.I, Revealing Intro. to HMM (Mark Stamp) Readings 3: PageRank
4	HMM for Tagging	HMM Notes Tagging Problem and HMM (M. Collins)
5	EM and Basics of Machine Translation	Note of EM for 3-Coin Problem Note of Basics of Machine Translation Reading 4: Maximum Likelihood Estimation
6	PCA and Latent Semantic Analysis	Lecture Note Reading 5: Chinese Word Segmentation, Paper by Sun and Paper by Huang Reading 6: Tutorial of PCA, PCA and SVD (Shlens)
7	Probabilistic Latent Semantic Analysis	Lecture Note T. Hofmann's original paper on P-LSA
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9	From TF-IDF to Information Retrieval
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Natural Language Processing and Intelligent Search