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Course Detail

Course Name Statistical Learning Theory
Course Code 18CS703
Program
Credits Coimbatore
Year Taught 2018

Syllabus

Course Syllabus

Overview of Supervised Learning, Basis Expansions and Regularization, Kernel smoothing, Model assessment and Selection, Model Inference, Additive Models, Trees & Related Methods, Boosting and Additive Trees, Support Vector Machines and Flexibilities, Prototype methods and Nearest Neighbors, Unsupervised Learning, Ensemble Learning, Undirected graphical Models, High dimensional Problems.

Course Outcome

At the end of the course the students will be able to;

Course Outcome Bloom’s Taxonomy Level
CO 1 Overview of Supervised Learning, Basis Expansions and Regularization L2
CO 2 Unsupervised Learning, Ensemble Learning L2
CO 3 Model assessment and Selection, Model Inference, Additive Models L3
CO 4 Support Vector Machines and Flexibilities L3
CO 5 Prototype methods and Nearest Neighbors, Undirected graphical Models, High dimensional problems L4
CO 6 Implementation of Additive models, SVM and its variants. L5

Text Books

  1. Trevor Hastie, Robert Tibshirani and Jerome Friedman, “Elements of Statistical Learning” Second Edition, Springer, 2008.

References

‘Statistical Learning Theory’ is an elective course offered for the M. Tech. in Computer Science and Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.

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