• 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
    10. Least squares solution for classification Slides Notes
    1. External References-I
    2. External References-II
    3. Bishop Texbook - Chapter 4.1.2
    11. Bias Variance Tradeoff Slides Notes Class Notes
    1. External References-I
    2. External References-II, Chapter-3 (Page number 147)
    12. Logistic Regression and K-class Softmax Regression Slides Notes
    1. Linear Models Vs Logistic Regression
    2. Why is it a linear model (even though, we had non-linear function)
    13. Bayes classifier - Probability of mistake Slides Notes
    1. Bishop Textbook Chapter 1 - section -1.5.2
    2. Bishop Textbook Chapter 4 - section -4.2
    3. Bayes classifier-slides
    14. Bias Correction in MLE estimation Classnotes & takehome Notes
    1. Maximum Likelihood Estimator for Variance is Biased: Proof
    2. Intuitive Explanation
    3. Bishop Textbook Chapter 1 - Exercise 1.12
    15. Multi-Variate Gaussian Classnotes & takehome Notes
    1. Maximum Likelihood Estimator for Variance is Biased: Proof
    2. Intuitive Explanation
    3. Bishop Textbook Chapter 1 - Exercise 1.12
    16. Naive Bayes Slides
    1. More Practice Questions on Naive Bayes
    17. Gaussian Mixture Model - EM algorithm Slides Notes
    1. Bishop Textbook Chapter 9,9.2
    18. Gaussian Mixture Model - II Class Notes
    1. Bishop Textbook Chapter 9,9.2
    19. Decision Trees for classification
    1. Slides
    2. Slides-II
    		Class Notes
    1. Exrernal References - I
    2. Exrernal References - II
    3. Exrernal References - III
    4. Exrernal References - III
    20. Support Vector Machine Slides Class Notes
    1. SVM - Cornell Notes
    2. External Reference
    21. Soft Margin SVM and Dual formulation Notes
    1. Softmargin SVM
    22. Tutorial on SVM link
    23. Kernel Methods and Kernel SVM Notes Question from Student
    1. Kernel Methods
    24. PCA and Basics PCA
    1. PCA visualization
    25. Tutorial on PCA link, PCA Examples
    26. Non linear dimensionality reduction Kernel PCA Questions from student
    1. Demo of PCA vs Kernel PCA
    2. Autoencoder as non linear PCA

    Tutorials

    S.N Material
    1. 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