Machine Learning Research at Johns Hopkins

• Machine Learning Group at Johns Hopkins

Textbooks

• 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
• David MacKay. Information Theory, Inference and Learning Algorithms. 2003 (Free Online)
• Michael Kearns, Umesh Vazirani. An Introduction to Computational Learning Theory. 1994
• Ethem Alpaydin. Introduction to Machine Learning. 2004
• Nils J. Nilsson. Introduction to Machine Learning (unpublished). 2005
• Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification.

Software

Thanks to Hal Daume for compiling most of this list.

• GRMM: A graphical models package add-on for mallet
• FastDT: Very fast decision tree learner that implements bagging and boosting
• libSVM: a very efficient library for SVMs, available in C and Java
• Mallet: a library for tons of NLP applications, including structured prediction with HMMs/CRFs, classification, clustering, topic modeling
• MegaM: Optimization software for maximum entropy models, uses conjugate gradient for binary/binomial problems and LM-BFGS for multiclass problems
• MinorThird: An NLP learning package that supports many standard algortihms
• NLTK: A super-easy to use Python implementation of many popular NLP algorithms
• SVM-Light: a super fast efficient library for SVMs. Supports ranking problems and kernels.
• Torch3: a generic machine learning library, particularly good for neural networks
• Weka: the "defacto" machine learning/datamining library

Machine Learning Courses

There are numerous machine learning courses offered at the undergraduate and graduate levels. Many courses post notes and slides that are useful for studying.

• CMU Machine Learning (Spring 2008, Fall 2008, Fall 2007)
• UPenn Machine Learning for NLP (CIS 630)
• NYU Machine Learning and Pattern Recognition (G22-2565-001, Fall 2005)
• MIT Machine Learning (6.867, Fall 2008)
• University of Utah Machine Learning (Fall 2009, Fall 2008, Spring 2007)
• A comprehensive list
• Stanford Undergraduate Machine Learning
• University College London Unsupervised Learning
• UPenn Machine Learning

Related Courses at Johns Hopkins

In addition to previous versions of this course, there are many other relevant courses at Johns Hopkins.

• Applied Mathematics and Statistics 550.437: Statistical Learning with Applications
• Artificial Intelligence
• Computer Vision Courses
• Natural Language Processing
• Machine Learning