Courses

September 26-27, 2006
Carnegie Mellon University, Pittsburgh, PA

Machine learning approaches to natural language processing problems such as information retrieval, document classification, and information extraction have developed rapidly over recent years. Even more recently, the joint analysis of text and images has become a significant focus for machine learning. This autumn school will summarize the state of the art in machine learning for text analysis and for joint text/image analysis, as presented by researchers active in these fields. It is intended for students who already have a familiarity with machine learning, and is designed for software developers, graduate students, and advanced researchers with an interest in learning more about this area.

Sessions include:

Text Analysis

• Text Classification
• Text Information Extraction
• Semisupervised Learning Approaches
• Information Retrieval
• Image Analysis & Graphical Model tutorial:

• Generative & Hierarchical Latent Space Models for Text and Image
• Generative Models for Visual Objects and Object Recognition via Bayesian Inference
• Joint Image/Text Analysis

• Joint Mining of Biological Text and Images: Cases Studies
• Undirected Graphical Models for Text & Image

www.ml.cmu.edu/summerschool/index.html