Tutorials
MIAS Data Sciences Summer Institute Tutorials
5/14/07 - 7/11/07
As part of the MIAS Data Sciences Summer Institute, renowned faculty from the University of Illinois Department of Computer Science will offer a collection of short courses designed to introduce students, scientists, and researchers to a variety of practices and software tools designed at UIUC in support of knowledge discovery. Because the tutorials are taught by some of the most innovative scientists in the world, the content will be up-to-the-moment and keenly relevant.
Enrollment in the tutorial includes the 15 hour course (over 5 days) and the opportunity to explore the possibility of collaboration with university faculty on projects of joint interest.
The tutorials are:
5/21-5/25
Databases and Information Integration
Kevin Chang
This tutorial instructs students in the fundamentals of DBMS and then takes them through a state-of-the-art tour of issues and techniques in data integration. Throughout, we emphasize modeling, query processing, semantic integration, and managing uncertainty and inconsistency. Modern techniques from the database, information retrieval, and artificial intelligence communities are applied in problems in data integration arising from the web, Deep-Web, and other inconsistent sources.
5/28-6/1
Information Retrieval and Web Information Access
ChengXiang Zhai
We introduce the foundation of information retrieval as well as its application and new development in Web information access. We will start with basic IR retrieval models for text retrieval. Then, we will study the new challenges of the Web and new techniques in Web search, integration, and mining--for finding information, integrating dynamic "deep" sources, and discovering knowledge.
Slides
6/4-6/8
Machine Learning
Mark Sammons and Nick Rizzolo
This tutorial will introduce methodologies and tools both for preparing data for use with machine learning tools (including feature extraction) and for applying machine learning techniques and tools to practical problems.
Examples will be given in the textual domain, starting with free-form text and building machine-learning-based natural-language processing tools. Participants will have the opportunity to develop tools to solve three text-processing problems during the three sessions.
Slides
6/18-6/22
Computer Vision
David Forsyth
The tutorial will be based around our textbook, "Computer Vision: A Modern Approach," which is now used in all major departments teaching the topic. The syllabus will emphasize aspects of computer vision most relevant to information discovery and retrieval. In particular, we will examine different technologies for image feature extraction, object recognition, camera calibration, and linking information in images with text information, metadata, and information in other formats.
Slides
6/25-6/29
Machine Learning and Data Mining
Jiawei Han
We will offer a tutorial course on data mining and machine learning, which introduces the concepts, algorithms, techniques, and systems of data mining and machine learning, including (1) data preprocessing, (2) frequent pattern and correlation analysis, (3) supervised learning (classification), (4) unsupervised learning (cluster analysis), (5) mining sequential and complex structured data, (6) mining data streams, text data, Web data, spatiotemporal data, biomedical data, and other forms of complex data, and (7) data mining and machine learning applications. The course may attract studednts from computer science and other disciplines who need to implement and/or use data mining and machine learning methods and systems to analyze large amounts of data.
Slides
