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Shirshendu Chatterjee

Current Teaching

  • Math A9804: Topics in Markov Chains
  • Math 19500: Precalculus

Upcoming Teaching

TBA

Past Teaching

Courses Taught at CUNY

Courses Taught at NYU

Courses Taught at Cornell University

Courses Assisted as a TA at Cornell University

Details of All Courses Taught


Topic Courses

  1. Math 83600, Topics in Machine Learning Methods and Data Science

    • Verbal Description: This course introduces the fundamental concepts and mathematical methods used in data science, modern statistics, and machine learning, including the description and theoretical analysis of several current algorithms, their theoretical basis, and associated mathematical frameworks. Many of the algorithms that will be discussed have been successfully used in various areas of real-world products and services.

    • Course Information.

    • Reference Books: Understanding Machine Learning: From Theory to Algorithms By Shai Shalev-Shwartz and Shai Ben-David; Foundations of Data Science By Avrim Blum, John Hopcroft, and Ravindran Kannan; Foundations of Machine Learning By Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar; The Elements of Statistical Learning By Trevor Hastie, Robert Tibshirani, and Jerome Friedman

    • Class Notes:

  2. Math B7700, Advanced Topics in Probability (Stochastic Processes)

  3. Math 83600, Topics in Probability (Markov Chains, Random Walks, and Brownian Motion)

  4. Math 83600, Topics in Probability (Nonspatial Random Graphs)

    • Verbal Description: This is a topic course for Ph.D. students that cover Branching Process and its properties, the story of the Erdos-Renyi random graph and its properties (particularly phase transition resulting in a giant component, connectivity threshold, scaling limits of near-critical graphs), other random graph models including configuration model, small world model, preferential attachment model etc.~and their properties (particularly diameter and local neighborhoods), introduction to dynamics taking place on random graphs such as epidemics, random walks, the voter model, first-passage percolation, competition models, etc.

    • Course Information.

    • Reference Books: Random Graph Dynamics By Durrett, Random Graph And Complex Networks Volume I By van der Hofstad

  5. Math B7800, Advanced Topics in Statistics (Regression Techniques)

    • Verbal Description: This course covers Multivariate linear regression models and associated statistical inference problems including the classical linear regression model, least square estimation, inference about the regression model, inference from the estimated regression function, model checking, variable selection, multivariate multiple regression, partial correlation, comparing regression models; Introduction to nonlinear regression including simple and multiple logistic regression;Principal component analysis including population principal components, large sample inference, use of principal components to summarize sample variation and quality control;Discrimination and Classification including multiple multivariate normal population, evaluation of classification functions.

    • Course Information. Class Notes.

    • Reference Books: Applied Linear Statistical Models By Kutner, Nachtsheim, Neter & Li.

    • Quizzes and Exams: Final Exam 2018.

  6. Math B7600, Advanced Topics in Statistics (Decision Theory)

    • Verbal Description: This course covers The general decision problem, Application to hypothesis testing and estimation (including Bayesian methods), Asymptotic evaluations of different inferential procedures, Analysis of variance, Regression models (including logistic regression) and associated inference.

    • Course Information.

    • Reference Books: Statistical Inference By Casella & Berger.

    • Quizzes and Exams:Final Exam 2016

Graduate Courses

  1. Math 70100, Functions Of A Real Variable I

    • Verbal Description: This is the first course on real analysis for the Ph.D. program.

    • Course Information, Course Syllabus.

    • Reference Books: Real analysis: modern techniques and their applications (2nd edition) By Folland, Principles of Mathematical Analysis (3rd Edition) By Rudin, Real Mathematical Analysis By Pugh.


  2. Math A7800/47800, Advanced Mathematical Statistics (Multivariate)

  3. Math A7800, Advanced Mathematical Statistics (Statistical Inference)

  4. ORIE 3510/5510, Introduction to Stochastic Processes

    • Verbal Description: This course introduces the concept of stochastic process to the undergraduate and M.Eng students, and gives an overview of the basic techniques used to analyze several standard models. I covered discrete time Markov chain, Poisson process, continuous time Markov chain, branching process, renewal theory including regenerative process, some queuing theory and a brief introduction to Brownian motion.

    • Course Information, Reference Books.

    • Class Notes:

    • Quizzes and Exams: Final Exam 2017

Undergraduate Courses

  1. Math 202, Calculus II

    • Verbal Description: This is a second semester calculus course for students who have previously been introduced to the basic ideas of differential and integral calculus. Topics that are covered include applications and methods of integration, infinite series and the representation of functions by power series, parametric curves in the plane.

    • Course Information.

    • Reference Book: Essential Calculus By Stewart.

    • Quizzes and Exams: Midterm, Final Exam.

  2. Math 203, Calculus III.

  3. Math 346, Linear Algebra.

  4. Math 366, Introduction to Applied Mathematical Computations.

  5. Math 375, Elements of Probability Theory.

  6. Math 376, Mathematical Statistics.

  7. Math 377, Applied Probability and Statistics.

    • Verbal Description: This course covered Essentials of the R Language including data input and output, data frame, graphics, tables, and functions; Simulation (using R) of random variables having various distributions ; Organization of data: measures of central tendency, variability, and order statistics; Understanding of hypothesis testing, p-values, and confidence intervals; Basics of classical diagnostic and statistical tests (normality test, t-test, Chi-Squared test, correlation test, rank-based nonparametric tests, Kolmogorov-Smirnov test, etc.) using R.Basics of the linear regression analysis using R; Basics of the contingency table analysis (Kolmogorov's exact test, goodness of fit test, etc.) using R;Basics of the ANOVA (analysis of variance) using R;Basics of the bootstrap and jackknife methods using R.

    • Course Information. Class Meeting Recirdings are available upon request.

    • Reference Book: The R Book By Crawley.

    • Quizzes and Exams: Quiz 1, Quiz 2, Quiz 3, Midterm 1, Midterm 2.

Independent Study Courses (Can be offered as Elective Courses)

  1. Math A9804, Topics in Markov Chains and Stochastic Processes

  2. Math A9802, Introduction to Math Models for Epidemiology

    • Verbal Description:This course introduces the fundamental mathematical epidemiological models. Topics that will be covered include basic concepts, compartmental models, endemic disease models, epidemic models.

    • Reference Books: Mathematical Models in Epidemiology By Fred Brauer, Carlos Castillo-Chavez, Zhilan Feng

  3. Math B9802, Processes on Networks

    • Verbal Description:

    • Reference Books: Random Graph Dynamics By Rick Durrett

  4. Math B9802, Network Models

    • Verbal Description:

    • Reference Books: Networks By Mark Newman

  5. Math B9802, Topics in Statistical Machine Learning (Modern Supervised Learning)

    • Verbal Description: This course covered Polynomial and nonlinear regression, Tree based inference methods, and Support vector machines.

    • Reference Books: An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani..

  6. Math B9802, Topics in Statistical Machine Learning (Regularized and Robust Regression)

    • Verbal Description: This course covered several linear methods of regression including the basic linear models, Ridge Regression, and the Lasso.

    • Reference Books: The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman.

  7. Math B9802, Topics in Deep Learning Procedures

    • Verbal Description: This course covered fundamentals of machine learning and deep learning, implementation of deep learning algorithms for texts and sequences.

    • Reference Books: Deep Learning with Python by Chollet; and Deep Learning by Goodfellow, Bengio, and Courville.

  8. Math B9802, Topics in Network Models and Data Analysis

    • Verbal Description: This course covered several mathematical and statistical models of network graphs, network topology inference, and network sampling methods.

    • Reference Books: Statistical Analysis of Network Data by Kolaczyk

  9. Math 31000, Topics in Linear Statistical Models and Implementations

    • Verbal Description: This course covered several topics of linear models and their implementations using R. The topics include variable selection, shrinkage methods, block design, factorial design.

    • Reference Books: Linear models with R by Faraway.

Teaching Assistantship

  1. ORIE 6320: Spring 2010

    • Course: Nonlinear programming

    • Instructor: Michael Todd

    • Verbal Description: This course is connected with the theory and algorithms for nonlinear programming, and it covers optimality criterions, convexity and duality, and algorithms for unconstrained, and linearly and nonlinearly constrained optimizations.

  2. ORIE 3500/5500: Fall 2008

    • Course: Engineering probability and statistics II

    • Instructor: Stefan Weber

    • Verbal Description: A rigorous formulation in theory combined with the methods for modeling analyzing and controlling randomness in engineering problems. Specific topics include random variables, probability distributions, density functions, expectation and variance, random vector, important distributions including normal, Poisson, exponential, hypothesis testing, confidence intervals, and point estimates using maximum likelihood and method of moments.

  3. ORIE 360/560: Fall 2007

    • Course: Engineering probability and statistics II/li>
    • Instructor: Sam Ehrlichman

    • Verbal Description: Topics include probability space, combinatorial probability, random variables and distributions, random vectors and independence of random variables, expectation, variance, correlation and higher moments and moment generating functions, standard inequalities and limit theorems, transformation of random variables, conditional expectations, multivariate normal distribution, sampling statistics, point and interval estimates.

  4. ORIE 270: Summer 2007

    • Course: Basic engineering probability and statistics

    • Instructor: Nikolay Bliznyuk

    • Verbal Description: This course introduces Statistics and Probability to the engineering undergraduate students and gives an overview of the basic techniques used in statistical analysis. It starts with the notion of survey, data collection, descriptive statistics and then introduces the concept of probability models. In Probability the course covers discrete and continuous random variables, their distributions, mean and variance, central limit theorem and linear combination of random variables and Normal approximation to binomial and Poisson. In Statistics it covers Point and interval estimation, hypothesis testing, two-sample t-test, paired t-test, introduction to linear regression, inference in regression analysis, regression diagnostics, multiple regression and ANOVA.

© Shirshendu Chatterjee.