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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.





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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