iPhone で読む (Machine Learning 系) Tutorial スライドの個人的なまとめ
iPhone で論文の pdf を読むのは，iPhone 4 の解像度でも画面のサイズ的に厳しそうなのだけど，スライドなら iPhone 3GS 程度の解像度でもストレスなく読めるので，機械学習系の Tutorial スライドをまとめてみた．NIPS/ICML などはビデオもあるのでそっちを見るのもありかも．内容的に被ってるのも差を見るために列挙．応用分野の研究者なので，かなり偏っています（ごく一部関係ないのも混ざってる）．
- Metric Learning (by B. Kuils; ICML 2010)
- Domain Adaptation (by J. Blitzer and H. Daume III; ICML 2010)
- Sparse Modeling: Theory, Algorithms and Applications (by I. Rish & G. Grabarnik; ICML 2010)
- Markov Logic in Natural Language Processing: Theory, Algorithms, and Applications (by H. Poon; NAACL 2010; ppt)
- Integer Linear Programming and Constrained Conditional Models for NLP (by M.-W. Chang, N. Rizzolo, & D. Roth; NAACL 2010; ppt)
- Survey of Boosting from an Optimization Perspective Part I, Part II (by M. K. Warmuth & S.V.N. Vishwanathan; ICML 2009)
- Reductions in Machine Learning (by A. Beygelzimer, J. Langford, & B. Zadrozny; ICML 2009)
- Machine Learning in IR: Recent Successes and New Opportunities (P. Bennett, M Bilenko & K. Collins-Thompson; ICML 2009)
- Active Learning by (S. Dasgupta & J. Langford; ICML 2009)
- Structured Prediction for Natural Language Processing (by N. Smith; ICML 2009)
- Sparse Methods for Machine Learning: Theory and Algorithms (by F. Bach; NIPS 2009)
- Deep Learning in Natural Language Processing (by R. Collobert & J. Weston; NIPS 2009)
- Learning to Rank (by H. Li; ACL-IJCNLP; 2009)
- Data-Intensive Text Processing with MapReduce (J. Lin; NAACL/HLT 2009)
- Constrained Conditional Models for NLP (by M.-W. Chang, L. Ratinov & D. Roth; EACL 2009; ppt)
- Query Log Mining (by F. Silvestri and R. Baeza-Yates; WWW 2009)
- Learning to Rank for Information Retrieval (by T.-Y. Liu; WWW 2009)
- Data-Intensive Text Processing with MapReduce (J. Lin; SIGIR 2009)
- Probabilistic Models for Information Retrieval Part I, Part II (by D. Metzler & V. Lavrenko; SIGIR 2009)
- Dimensionality Reduction the Probabilistic Way (by N. D. Lawrence; ICML 2008)
- Beyond Convexity: Submodularity in Machine Learning (by A. Krause and C. Guestrin; ICML 2008)
- Theory and Applications of Online Learning (S. Shalev-Shwartz and Y. Singer ; ICML 2008)
- Sparse Linear Models: Bayesian Inference and Experimental Design (M. W. Seeger; ICML 2008)
- Semi-supervised Learning for Natural Language Processing (by J. Blitzer; ACL-08:HLT)
- Advanced Dynamic Programming in Computational Linguistics: Theory, Algorithms, and Applications (by L. Huang; COLING 2008)
- Learning to rank for information retrieval (by T.-Y. Liu; WWW 2008)
- Opinion Mining and Summarization on the Web (by B. Liu; WWW 2008)
- (by W. Fan and M. Sugiyama; ICDM 2008; ppt)
- Online Learning for Real World Problems (by K. Crammer; ICML 2007; pps)
- Semi-Supervised Learning (X. Zhu ; ICML 2007)
- Theory and Applications of Boosting (by R. Schapire; NIPS 2007)
- Learning Using Many Examples (by L. Bottou & A. Moore; NIPS 2007)
- Structured Prediction (by B. Taskar; NIPS 2007; ppt)
- Bayesian Nonparametric Structured Models (by P. Liang and D. Klein; ACL 2007)
- Hashing, Sketching, and Other Approximate Algorithms for High-Dimensional Data (by P. Indyk; EMNLP-CoNLL 2007)
- Machine Learning for Natural Language Processing: New Developments and Challenges (by D. Klein; NIPS 2006)
- An SVM Approach to Natural Language Learning (by M. Collins; CoNLL-X)
- Clustering with Constraints: Theory and Practice (by S. Basu & I. Davidson; KDD 2006)
