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Teaching
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Artificial Intelligence (For
undergraduate students) Details
Text Book, Artificial
Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall,
Englewood Cliffs, New Jersey, 2003.
Artificial
Intelligence (AI) is still a research discipline in attempting to
understand the mechanisms underlying intelligent behavior and to build
"intelligent systems" from variety of mechanical and electronic
devices. This course is to offer an introduction to artificial intelligence
covering from mechanism, models, algorithm to some typical AI applications
as well. The course AI covers the following interesting topics: a brief
history of AI, research and philosophical questions faced by AI
practitioners, representing and solving AI problems in a state space search
formalism, heuristics, connectionism, and specific AI problems such as
vision, natural language and robotics.
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Statistical Learning (For Graduate Student) Details
Text Book: Elements of
Statistical Learning, Hastie T., R. Tibshirani, and J. Fiedman, Springer,
2001
Statistical Learning and
Inference focuses on the statistical features of machine learning and
inference. This course introduces basic theory and methods for extracting
rules, structures and patterns in large scale data, requiring students to
master system modeling, parameter identification and model inference based
on statistical models. The statistical learning methods are applicable to
broad areas such as data mining, artificial intelligence and natural
language processing. The course features to provide project practice on
large scale data to master capability of solving large scale practical
problems through modeling and learning.
The course is
suitable for the master degree students working on intelligent information processing,
pattern recognition, data mining and bioinformatics.
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