Machine Learning

Course Offerings

  1. Monsoon 2020: CSE343/ECE343
  2. Monsoon 2019: CSE343

Course Overview

This is an introductory course on Machine Learning (ML) that is offered to undergraduate and graduate students. The contents are designed to cover both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets. The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training and validation, testing and evaluation with quantitative performance comparisons.

Course Objectives

  • Explain the different types of learning problems along with some techniques to solve them.
  • Model real-world problems, apply different learning techniques and quantitatively evaluate the performance.
  • Identify and use advanced techniques through existing machine learning tools and libraries.
  • Analyze performance of ML techniques and comment on their limitations.

Course Syllabus

  1. Introduction: Introduction to Machine Learning; Deep Learning vs Machine Learning; Review
  2. Empirical Risk Minimization: Learning Problems and the Risk Minimization Framework;
  3. Regression: Linear and Logistic Regression
  4. Machine Learning in Practice: Bias-Variance; Training/Testing; Overfitting; Jackknifing/Cross-Validation; Occam’s razor; Regularization and Model Selection
  5. Classification: Naïve Bayes; Decision Trees; Random Forests
  6. Neural Networks: Perceptron; Backpropagation; Training Neural Networks
  7. Support Vector Machines: Optimization basics; Primal Form; Dual Form; Kernel SVMs, Soft-Margin SVMs
  8. Unsupervised Learning: k-means clustering; Spectral Clustering (if time permits)
  9. Deep Learning: Convolutional Neural Networks and applications to Images and Speech.
  10. Ethical Issues: Fairness, Accountability, and Transparency in Machine Learning


Related Journals and Conferences [!Exhaustive]

Course Administration

  • Google Classroom will be used as primary mode of course administration, including general announcements, announcements/submission of assignments, doubt resolution.

  • Colab will be used for Coding Assignments.
  • Course will be supported with teaching assistants (TAs). TAs will communicate their office hours individually.
  • TAs will support the course through doubt resolution, responsible for announcement and/submission of quizzes/assignments, grading with faculties supervision etc.

Plagiarism Policy

  • The course follows a zero tolerance on academic dishonesty. The course follows the IIIT-Delhi policy on Academic Dishonesty [Updated Institute policy will apply]
  • All plagiarism cases will be on record for your tenure at IIIT-Delhi
  • All code and reports will be checked for plagiarism
  • Home work theory questions may be asked in exam or quiz as it is
  • If correct in Home work and incorrect in exam, home work question will be marked zero. Make sure you know all your home work theory questions.

Auditing Requirements

  • Students need to submit all assignments and projects on time.
  • Students should achieve a grade of at least the class average.
  • Audit students are not permitted to do projects with registered students.
  • Quizzes and Exams are optional.