Academic Notes
This page contains all my notes that I have made during my studies at ISI till now. They are written in LaTeX of which you'll be seeing the rendered pdf versions here. Make best use of them!
Semester 03
Since Prof. Arnab Chakraborty at ISI Kolkata has already provided such comprehensive and well-crafted notes for this course, it feels redundant to recreate them from scratch, so I will simply link directly to his website instead.
📄 Professor's NotesCompleted
The course started with a review of Simple Linear Regression and then we proced towards Multiple Linear Regression. I'm also studying so I'm unsure what kept in here in future;)
📄 View PDF NotesLast Updated on August 3, 2026
This course aims at covering Linear Algebra, which covers parts of Linear Transformation, Real and Complex Inner Product and SVD, Graph Theory and Basics of Topology
📄 View PDF NotesLast Updated on August 7, 2026
Summer 2026
This document contains detailed notes of my studies of Statistical Inference during the Summers of 2026. I have utilised the following resources while making these notes: Statistical Inference by Prof. Somesh Kumar IIT KGP (NPTEL) and Statistical Inference by George Casella and Roger Lee Berger.
📄 View PDF NotesLast Updated on June 18, 2026
Semester 02
Arguably the toughest course in the semester. This document starts with the fundamentals of probability theory and the need for parametric inference. We then proceed to Point Estimation while covering Convergence in Mean, Probability and Distribution, Testing of Hypothesis, Central Limit Theorem and finally culminating in Interval Estimation.
📄 View PDF NotesCompleted
This document covers in depth the principles and applications of convex optimization including convex set, convex function, linear programming, KKT conditions, and duality. In addition to that, we have also explored some numerical methods such as root finding methods, polynomial interpolation and numerical integration.
📄 View PDF NotesCompleted
Semester 01
A deep dive into descriptive statistics, measures of central tendency and dispersion, moments, skewness, kurtosis, and the fundamentals of simple linear regression.
📄 View PDF NotesCompleted
Axiomatic foundations of Kolmogorov probability, counting techniques, conditional probability, Bayes' Theorem, and probability mass functions.
📄 View PDF NotesCompleted
We had an introduction to microeconomic concepts in this course.
📄 View PDF NotesCompleted