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The Department of Computer Science at the University of Illinois at Urbana Champaign has several faculty members working in the area of machine learning, learning theory, explanation based learning, learning in natural language processing and data mining. In addition, many faculty members inside and outside the department whose primary research interests are in other areas have specific research projects involving machine learning in some way.


  • CS446: Machine Learning
    • The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others.

  • CS546: Machine Learning and Natural Language
    • Making decisions in natural language processing problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. Structured learning problems such as semantic role labeling provide one such example, but the setting is broader and includes a range of problems such as name entity and relation recognition and co-reference resolution. The setting is also appropriate for cases that may require a solution to make use of multiple models (possible pre-designed or pre-learned components) as in summarization, textual entailment and question answering.

  • ECE 598: Statistical Learning Theory
    • Advanced graduate course on modern probabilistic theory of adaptive and learning systems. The following topics will be covered: basics of statistical decision theory; concentration inequalities; empirical risk minimization; complexity-regularized estimation; generalization bounds for learning algorithms; VC dimension and Rademacher complexities; minimax upper and lower bounds for classification and regression; basics of online learning and optimization. Along with the general theory, the course will discuss applications of statistical learning theory to signal processing, information theory, and adaptive control.

  • STAT 542 Statistical Learning
    • distinguishes itself by its focus on regularized learning, on the types of regularization that can be used for a wide variety of learning problems.

  • CS591: Seminar on Learning and Knowledge

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