CS5103: Applied Machine Learning

Jul-Dec, 2025


Table of Contents

Lectures

Syllabus
Caution: Kindly pay attention to the comments in the Jupyter notebooks as well as classroom discussions.
In the exams, questions will also come from there, not only from the slides.
Event Date Description References
Lecture 1 Aug 1 Course Introduction and Python Recap [ Slides, Notebook ]
Lecture 2 Aug 5 Intro to Scikit-learn [ Slides, Notebook ]
Lectures 3, 4 Aug 6, 12 Intro to TensorFlow [ Slides, Notebook ]
Lecture 5 Aug 13 A Brief Review and Comparative Study of Classification Algorithms [ Notebook ]
Lecture 6 Aug 20 Recognizing Hand-written Digits Using a Support Vector Classifier [ Notebook ]
Lecture 7, 8, 9 Aug 22, 26, 27 Understanding the Maximal Margin Classifier and Support Vector Classifier [ Slides ]
Lectures 10, 11 Aug 29, Sep 2 Investigating the Class Boundaries Learned by LDA vs. that of QDA [ Notebook ]
Lecture 12 Sep 3 Stock Market Prediction with a Naive Bayes Classifier [ Notebook ]
Exam Prep Sep 11 Sample Questions with Answers for the Mid-Sem Exam [ PDF ]
Exam Sep 13 Mid-Sem [ Qn Paper ]
Exam Solutions Sep 16 Discussing Solutions of the Mid-Sem Exam [ Solution ]
Lecture 13 Sep 17 Stock Market Prediction with a K-Nearest Neighbours (KNN) Classifier [ Notebook ]
Lecture 14 Sep 19 Linear Regression and Poisson Regression [ Notebook ]
Lectures 15, 16, 17 Sep 23, 24, 26 Extracting Features from Texts and Images [ Slides ]
Lectures 18, 19, 20, 21, 22, 23 Sep 30; Oct 3, 7, 8, 10, 14 More Regression Methods: Ridge, Lasso, Principal Component Regression (PCR), Partial Least Squares (PLS) [ Notebook ]
Lectures 24, 25, 26 Oct 15, 17, 21 Decision Trees, Bagging, Random Forests, Boosting [ Notebook ]
Lecture 27 Oct 22 One-Class, Two-Class, Multi-Class Classifications with Support Vector Machines (SVMs) [ Notebook ]
Lectures 28, 29 Oct 24, Nov 4 K-Means Clustering and Hierarchical Clustering [ Notebook ]
Lectures 30-32 Oct 25, 26, 28 Preface to Artificial Neural Networks (ANNs),
Perceptron Implementation from Scratch		 Not Publicly Shared
Lecture 33 Nov 7 Graphical Models [ Notebook ]
Lectures 34-39 Nov 18-27 Multilayer Perceptron (MLP) to Large Language Models (LLMs) Not Publicly Shared
Exam Nov 29 End-Sem [ Qn Paper ]

Text and Reference Book(s)

  1. ISLP: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor (2023), An Introduction to Statistical Learning: With Applications in Python (ISLP), Springer. website
  2. Bishop: Christopher M. Bishop (2006), Pattern Recognition and Machine Learning, Springer.
  3. Duda-Hart: Richard O. Duda, Peter E. Hart, David G. Stork (2000), Pattern Classification, Second Edition, Wiley.
  4. Marsland: Stephen Marsland (2015), Machine Learning: An Algorithmic Perspective, Second Edition, CRC Press.

Similar Courses

Journals

Conferences

Social Media Channels

Blogs

Projects

© Saptarshi Pyne