CS6464: Concepts In Statistical Learning Theory

January - May, 2018

Course Contents

  • Objectives:
    • In the recent past, algorithms of solving many ill-posed problems in the field of multi-dimensional signal processing and big data analytics have gained importance. New methods of signal representation, modeling, optimization and leaning have been formulated, which spans over various areas of Machine Learning, Pattern Recognition, Vision and Natural Language Processing. This course will provide an overview of the theories and current practices, required by students and scholars who intend to specialize in this field, to solve complex problems in Machine Learning Applications for image, video, text and bioinformatics.
  • References

    V. N. Vapnik; Statistical Learning Theory. Wiley, 1998.
    T. Hastie, R.Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference and Prediction", Springer Series in Statistics, 2009.
    Kevin R Murphy, "Machine Learning - A Probabilistic Perspective", The MIT Press, 2012.

    Michael J. Kearns and Umesh Vazirani; An Introduction to Computational Learning Theory; The MIT Press, 1994.
    Journal of the Royal Statistical Society: Series B (Statistical Methodology).
    Foundations and Trends in Machine Learning; Now Publishers Inc.
    Journal of Machine Learning Research; JMLR, Inc. and Microtome Publishing (United States).
    Bishop, Christopher M. "Pattern recognition and machine learning", Springer, 2006.
    Conference Proceedings of ICML, NIPS.
    R.O. Duda, P.E. Hart and D.G. Stork "Pattern Classification (2nd ed.)", John Wiley & Sons, Inc., 2003.

    Lecture Slides

    Basic Statistics and Theorems  Download Slide
    Least Square Regression, Bias-Variance Tradeoff  Download Slide
    Clustering Techniques  Download Slide
    LAR and LASSO  Download Slide
    L_1 regularization techniques  Download Slide
    Sparse Coding  Download Slide
    SVM  Download Slide


    Software Assignment 1
            Problem Statement : Download Slide
            Data for Question 1 : Q1_data_01.Rda    Q1_data_02.Rda

    Software Assignment 2
            Problem Statement : Download Slide         Allotment of learning techniques : Download Slide
            Click here to go the Data Repository page

    Software Assignment 3
            Problem Statement : Download Slide
            Dataset : Assignment3Data   


    Marks Distribution

    Tutorial Dates

    Logistic Details

    Important Dates
    Extra Classes:
    February 17,2018 (10:30am-12:30pm) & April 8,2018 (10:30am-12:30pm)
    Mid Semester Exam:
    March 21,2018 (3:30-4:30pm)
    End Semester Exam:
    May 11,2018 (9:00am-12:00pm)
    Software Assignment 1:
    February 15,2018
    Software Assignment 2:
    March 15,2018
    Software Assignment 3:
    April 27,2018


    Self Study Topics

    Useful Links

    Statistical Help
    Linear Model Selection and Regularization