• Instructor: Parimala Kancharla: (office: A17.03.06, email: parimala@iitmandi.ac.in)
  • Office Hours: (or by appointment)
  • Discussion Forum: Piazza code-ds413 ,To Join
  • Class Venue: A18-1
  • Class Timings: A-slot
  • Syllabus
  • TAs
    1. Pallav Dwivedi (s23104@students.iitmandi.ac.in)
    2. Siddharath Shakya (s23048@students.iitmandi.ac.in)
    3. Shilpa Chandra (S22004@students.iitmandi.ac.in)
    4. Soujatya Sarkar (s23106@students.iitmandi.ac.in)
    5. Kriti Khare (s23069@students.iitmandi.ac.in)

    Evaluation

  • Assignments - 25%
  • Tutorials/Quizzes - 15%
  • Take home questions (Open book)- 5%
  • Midsem - 25%
  • Endsem - 30%

    • Resources

    S.N Topic Slides Notes External References
    1. Introduction Slides ..
    1. ML Vs LLMs
    2. Traditional AI Vs Deep Learning
    2. Basic Math Review Slides Notes
    1. Is Relu Differentiable?
    2. RELU is not differentiable,why?
    3. Numerical Computation
    3. Basic Math Continuation Slides Notes ..
    4. Basics of Probability Slides ..
    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. PRML-Textbook- Bishop-Chapter-1
    7. Ridge and Lasso Regression Slides
    1. Notes
    2. Class 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. Least squares solution for classification Slides Notes
    1. External References-I
    2. External References-II
    3. Bishop Texbook - Chapter 4.1.2
    10. Bias Variance Tradeoff Slides Notes
    1. External References-I
    2. External References-II, Chapter-3 (Page number 147)
    11. Bias Variance Tradeoff -II 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. Logistic Regression Continued Notes
    1. Linear Models Vs Logistic Regression
    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. Multivaraite Gaussian - ML estimation Notes
    1. Multivaraite Gaussian Bayes Classifier - Slides
    2. MLE for multivariate Gaussian-Bishop Textbook Chapter-4,4.2
    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. Decision Trees for Regression .. Class Notes
    1. Decision Tree using Gini Index
    2. Decision Tree - Regression
    21. Support Vector Machine Slides Class Notes
    1. SVM - Cornell Notes
    2. External Reference
    22. Soft Margin SVM and Dual formulation Notes
    1. Softmargin SVM
    23. Tutorial on SVM link
    24. Model Selection and Model Evaluation Slides
    1. Slides on Cross Validation
    25. Kernel Methods and Kernel SVM Notes Question from Student
    1. Kernel Methods
    26. PCA and Basics PCA
    1. PCA visualization
    27. Tutorial on PCA link, PCA Examples
    28. Non linear dimensionality reduction Kernel PCA Questions from student
    1. Demo of PCA vs Kernel PCA
    2. Autoencoder as non linear PCA
    29. Ensemble Learning - Bagging & RF Slides Class Notes
    1. Bishop Textbook - Chapter 14
    30. Adaboosting Slides Class Notes
    1. Weighted decision tree example
    31. Previous Year Papers link
    32. Gradient Boosting Class Notes
    1. Gradient Boosting
    *** Practice Questions Link
    *** Self Study Topics(not included in syllabus, to be updated)

    Recorded Lectures

    S.N
    Lecture Recordings

    Assignments

    S.N Assignmnet Release Date Submission Date
    1. Assignment-1 17/08/2024 31/08/2024
    2. Assignment-2 11/09/2024 05/10/2024
    3. Assignment-3 16/10/2024 10/11/2024

    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

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