Soumendu Sundar Mukherjee

Welcome to my homepage! I am currently an INSPIRE Faculty at the Interdisciplinary Statistical Research Unit (ISRU) at Indian Statistical Institute, Kolkata.

I am primarily interested in various aspects of Machine Learning, Probability and Statistics. Current research interests, among other topics, include statistical analysis of network data, changepoint detection, high-dimensional statistics, random matrix theory and neural networks.

In Spring 2021, I am teaching Weak Convergence and Empirical Processes.

Recent posts

Introduction to Classification March 18, 2021

Classification is one of the most basic problems in Statistics and Machine Learning. It is an example of a supervised learning problem where one deals with labeled data. In this note, we will learn about basic classification algorithms such as

  1. Logistic regression
  2. Linear and quadratic discriminant analysis
  3. Naive Bayes
  4. Multinomial logistic regression
  5. \(k\)-nearest neighbours
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Stochastic Processes: Notes 3 November 18, 2020

Continuous time Markov chains

You are familiar with discrete time Markov chains. In this note, we will consider the continuous time version, where a chain can stay in a state for a random (continuous) amount of time. We will see that the Markov property forces that this random time is exponentially distributed. Given this, it is not surprising to guess that the homogeneous Poisson process \(\mathrm{PP}(\lambda)\) is a continuous time Markov chain.

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Stochastic Processes: Notes 2 October 27, 2020

In this note we will talk about Poisson point processes. There are many measure theoretic issues involved which we will silently hide under the carpet.

Poisson processes on \([0, \infty)\)

Poisson processes are the most prominent examples of counting processes, processes that count the number of events in some time interval.

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