• Instructor: Parimala Kancharla: (office: A17.03.06, email: parimala@iitmandi.ac.in)
  • Office Hours: ( by appointment)
  • Discussion Forum: Piazza code-ds413 ,To Join
  • Class Venue: A17-1B
  • Class Timings: A-slot
  • Syllabus
  • TAs
    1. Mridul Sharma, d24003@students.iitmandi.ac.in
    2. Abhishek Dileep, s23103@students.iitmandi.ac.in
    3. Pallav Dwivedi, s23104@students.iitmandi.ac.in
    4. Ajinkhya Hase, s24089@students.iitmandi.ac.in
    5. Satish Maurya, s24012@students.iitmandi.ac.in

    Evaluation

  • Assignments - 15%
  • Quizzes - 10%
  • ML Competition - 20%
  • Midsem - 25%
  • Endsem - 30%
  • Attendance - As per the Institute Norms

    • Resources

    S.N Topic Slides Notes External References
    1. Introduction Slides ..
    1. ML Vs LLMs
    2. Traditional AI Vs Deep Learning
    2&3. Basic Math Review
    1. Slides
    2. Slides
    1. Notes
    2. Notes
    1. Is Relu Differentiable?
    2. RELU is not differentiable,why?
    3. Numerical Computation
    4. Basics of Probability Slides Notes
    1. Basics of Probability
    2. Probablity chapter from Deep learning by Ian Goodfellow
    5. Linear Regression Slides
    1. Notes
    2. Class Notes
    1. Textbook-The Elements of Statistical Learning-Chapter-3
    2. PRML-Textbook- Bishop-Chapter-3
    6. Polynomial Regression Slides
    1. Notes
    2. Class Notes
    1. Textbook-The Elements of Statistical Learning-Chapter-3
    2. PRML-Textbook- Bishop-Chapter-3
    7. Ridge and Lasso Regression Slides
    1. Notes
    1. Textbook-The Elements of Statistical Learning-Chapter-3
    2. Why L1-regularization gives us sparse models
    8. Q.N on Lasso and Ridge Regression .. Notes Visualization of regularization
    9. Ridge Regression-II .. Notes Chpater3- Elements of Statistical Learning

    Tutorials

    S.N Material
    1. To be Updated

    Assignments

    S.N Assignmnet Release Date Submission Date
    1. To be Updated To be Updated To be Updated

    Text books

    1. Pattern Recognition and Machine Learning Book by Christopher Bishop Link
    2. Introduction to Machine Learning by Ethem Alpaydin Link
    3. The Elements of Statistical Learning by Trevor Hastie Robert Tibshirani Jerome Friedman Link