Laboratory of Statistical Artificial Intelligence and Machine Learning

Machine Learning DS5006 Fall 2026

Description

Machine Learning (ML) is the study of computer algorthms that learn and imrpove automatically through experience. ML is an increasingly popular subject due to a wide variety of applications such as autonomous vehicles, hand-written character recognition, automatic speech processing, recommendation systems, etc. This is a relatively advaned counterpart of DS3010, with emphasis on the underlying mathematics of the algorithms. The course discusses some of the basic and widely used ML techniques, covering a wide range of topics such as supervised and unsupervised learning, classification and regression, support vector machines, and dimensionality reduction. A complete list of topics covered in the course can be found in the course schedule.

Lecture and Lab Timings

Classroom A01-212 Tuesday 3.30-4.45pm
Thursday 2.00-3.15pm
Wednesday 2.00-3.30pm (most of the labs will be take home)

Reference Material

There is no fixed textbook for the course. The lectures will adopt content from the following textbooks:

Machine Learning by Tom Mitchell
Introduction to Machine Learning by Ethem Alpaydin
Elements of Statistical Learning by Trevor Hastie, Rogert Tibshirani, and Jeroe Friedman
Pattern Recognition and Machine Learning by Christopher Bishop

Academic integrity

Students enrolled in this course are expected to exhibit a strong desire to learn, rather than just fulfilling a requirement for their degree. Engaging in discussions that help students better understand concepts or problems is encouraged. However, all submitted work must be original. Plagiarism, including copying from the internet, textbooks, GenAI Tools or any other source for which the student does not hold the copyright, as well as sharing code with other students, will not be tolerated and will result in strict disciplinary action, including a failing grade in the course. If you have any questions about this policy, please contact the instructor. All academic integrity violations will be handled in accordance with institute regulations.

Grading Policy

Tests Two tests will be conducted during the semester. Check the course calendar for the quiz dates. Each quiz will account for 20% of your overall grade.

Labs There will be 5 programming labs during the semester that will account for 20% of the overall grade. All labs are due on Thursday of the week.

Lab Exam There will be a 2 lab exams one in the middle of the semester and one towards the end of the semester that will account for 10% of the overall grade.

Project There will be a semester long project that will account for 10% of the overall grade.

Exams There will be an end-semester exam that will account for 40% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Schedule

week 1 (1.5) - Introduction and Supervised Learning -
week 2 (3) - Linear Regression
week 3 (3) - Linear Classification, lab 1
week 4 (3) - Decision Tree Learning
week 5 (3) - Kernel Methods
week 6 (3) - Kernel Methods lab 2
week 7 (3) - Buffer week, test 1
week 8 (3) - Experimental Design and Model Selection, Lab exam 1
week 9 (3) - Experimental Design and Model Selection lab 3
week 10 (3) - Ensemble Methods - Boosting and Bagging
week 11 (3) - Unsupervised Learning - k-means Clustering
week 12 (3) - Gaussian Mixture Models and Expecctation Maximization, test 2
week 13 (1.5) - Dimensionality Reduction: Principal Component Analysis, lab 4
week 14 (3) - Linear Discriminant Analysis
week 15 (1.5) - Summary, lab 5
week 16 (3) - Project Presentations, Lab exam 2

Lecture Material

Students enrolled in the course can access the lecture material from Moodle