Teaching

This contains some of my lectures, tutorials and teaching materials. If you find an error, I am the one responsible to take the blame, but I would love if you please submit a [pull request] in Github to correct the error. You can also get in touch with me here.

1 Teaching Materials

The following contains some lecture notes, tutorials and teaching materials, organized at various levels. These include my own personal notes and reflect my own understanding of various topics.

• If you’re a student of 10+2 level looking for mathematics notes, see Section 1.1.
• If you’re an undergraduate looking to learn Statistics, see Section 1.2.
• If you’re a graduate level student looking for notes on Statistics, Machine Learning, or Deep Learning related topics, see Section 1.3.
• If you’re a practitioner of Machine Learning, Statistics or Data Science and want to learn the high level concepts without digging into too much math, see Section 1.4.
• If you’re a researcher, I can only offer you notes from Section 1.5.

1.1 10+2 Level

• A short notes on Riemann Integration.

• Practice Problem Sets for 10+2 Level Statistics (West Bengal State Board).

  • Problem Set 1
  • Problem Set 2

• Practice Problem Sets for B.Stat. Entrance Exam for Indian Statistical Institute.

  • MCQ Set 1
  • MCQ Set 2
  • MCQ Set 3
  • MCQ Set 4
  • MCQ Set 5
  • SAQ Set 1
  • SAQ Set 2
  • SAQ Set 3

1.2 Undergraduate Level

• Practice questions and solutions to various assignments of the B.Stat. programme at Indian Statistical Institute, Kolkata.

• A short lecture slide deck on Linear Algebra and its applications.

1.3 Graduate Level

• Practice questions and solutions to various assignments of the M.Stat. programme at Indian Statistical Institute, Kolkata.

• An eight-part tutorial series on Natural Language Processing is available at my substack.

• A six-part tutorial series on the basics of Generative AI is available at my substack.

• A seven-part tutorial series on the basics of Reinforcement Learning is available at my substack.

• A three part series on how to do natural language text processing using R.

  • Text mining in R.
  • Text Classification in R.
  • Changepoint Analysis of Linguistics in R.

1.4 Practitioner Level

These reflect my own understanding and notes on various applied topics, as guided by the various resources I used to learn them.

1.5 Researcher Level

These are some of the useful proof techniques that I like and a bunch of technical materials that I have collected over the years.

2 Teaching Experience

Here’s a list of my teaching experiences.

2.1 Lectures

1. Fall, 2026 - Elementary Statistics and Probability (SDS 2020, Undergraduate Level) | Washington University in St. Louis.

2. Spring, 2026 - Advanced Linear Models (SDS 4140 & SDS 5140, Graduate Level) | Washington University in St. Louis.

3. Fall, 2025 - Introduction to Probability (SDS 4010, Undergraduate Level) | Washington University in St. Louis.

2.2 Guest Lectures

1. Spring, 2026 - Linear Models (SDS 5072, Ph.D Level) | Washington University in St. Louis.

2. Fall, 2024 - Robust Statistics | Indian Statistical Institute, Kolkata | Slide deck

3. Fall, 2022 - Robust Statistics | Indian Statistical Institute, Kolkata | Slide deck