Statistics

This course introduces core ideas in probability and statistics with a focus on building a foundation for data science.   Topics include probability theory basics, common probability distributions, sampling and estimation, confidence intervals and hypothesis testing, and simple linear regression, resampling methods, smoothing techniques, and an introduction to the Bayesian inference. The course also offers a brief introduction to programming in R and Python.

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024. 

This is only recitation for STAT UG1201. We are requesting 8 sections of recitation to align with the THREE sections of 1201 offered for Fall 2026.

This is only recitation for STAT UG1201. We are requesting 8 sections of recitation to align with the THREE sections of 1201 offered for Fall 2026.

Prerequisites: one semester of calculus. Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412.

Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful. This course is a machine learning class from an application perspective. We will cover topics including data-based prediction, classification, specific classification methods (such as logistic regression and random forests), and basics of neural networks. Programming in homeworks will require R.

This course provides a non-mathematical introduction to the principles and architectures of deep learning and generative AI models. Designed for undergraduates in the Applied Data Science minor, the curriculum covers the mathematical foundations of neural networks and their application to spatial, temporal, and multimodal data. Students will examine the mechanics of convolutional and recurrent architectures, the self-attention mechanism in Transformers, and the training objectives of Large Language Models (LLMs). The course also addresses optimization strategies, reinforcement learning for model alignment, and generative paradigms, including diffusion and autoregressive models. Emphasis is placed on understanding model internal representations, architectural tradeoffs, and the evaluation of complex AI systems.

Course Overview: This course introduces Python programming, covering data structures, control-flow, objects, and functions, along with libraries like re, requests, numpy, pandas, scikit-learn, scipy, and more. These skills are applied to real-world data science tasks, including AB testing, data manipulation, modeling, optimization, simulations, and data visualization.

Students will develop computational thinking abilities, including problem decomposition, pattern recognition, data representation, abstraction, and algorithm design, through practical exercises.

This course covers various topics in advanced machine learning. Topics may include optimization algorithms, Python libraries for ML, principles for applied supervised and unsupervised learning, hyperparameter selection, computational trade-offs, modern neural network architectures such as ConvNets, LSTMs, and transformers for computer vision and natural language processing, and deep learning for LLMs.

Topics in Modern Statistics provide students with an opportunity to study a specialized area of statistics in more depth to meet the educational needs of a rapidly changing field.

Topics in Modern Statistics provide students with an opportunity to study a specialized area of statistics in more depth to meet the educational needs of a rapidly changing field.

Topics in Modern Statistics provide students with an opportunity to study a specialized area of statistics in more depth to meet the educational needs of a rapidly changing field.

Topics in Modern Statistics provide students with an opportunity to study a specialized area of statistics in more depth to meet the educational needs of a rapidly changing field.

This course is an introduction to Causal Inference at the masters level. Students will be introduced to a broad range of causal inference methods including randomized
experiments, observational studies, instrumental variables, di?erence-in-di?erences, regression discontinuity design, and synthetic controls. In addition, the course will cover modern, controversial debates regarding the foundations and limitations of causal inference.

The primary learning goal of this course will be to familiarize students with a variety of the most popular causal inference methods: which causal e?ects they seek to estimate, basic assumptions required for identi?cation and estimation, and their practical implementation. To this end, the course will focus both on developing the pre-requisite statistical / methodological theory and as well as gaining hands-on experience through implementation exercises with real datasets. By the end of the course, students should have deep familiarity of various causal inference methods and—more importantly—be able to determine which method is most appropriate
for a given applied problem and to judge whether the pre-requisite identifying conditions are appropriate.

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

This upcoming fall, we are going to kick off the “Practitioners Seminar” course, where successful practitioners from various industry fields (tech, finance, insurance, pharmaceutical, etc..) will have a chance to meet our students and present the projects they work on, technologies they utilize to achieve their goals, solutions they came up with etc. In addition, guest speakers will share their career development path (what kind of obstacles they faced, what pitfalls to avoid, and in general give advice on career development in their fields). We will finish up the meeting with a Q&A session with students.

This high-level course in linear regression delves deeply into the theoretical and geometric aspects of regression analysis, offering a comprehensive exploration of its foundational principles and advanced topics. Students will study regression within vector space contexts, emphasizing the role of inner products and orthogonal projections. The analysis of projection matrices will include their properties, such as idempotence and symmetry, and their implications for regression diagnostics and metrics. Students will explore why various test statistics follow t- and F-distributions, with careful attention to degrees of freedom and their derivations. As the course progresses, it will address the complexities of high dimensional regression scenarios.

Each year, approximately 10–15% of MA in Statistics students participate in on-campus academic research, contributing to advances in statistical methodology and applied areas. Some projects demonstrate exceptional promise and benefit from additional time and support for further development. The MA Research Specialization in Statistics allows qualified students to extend their MA program to a fourth semester to continue their research under the supervision of a faculty mentor. This competitive, merit-based program requires demonstrated research progress, a nomination from a faculty mentor, and an outstanding academic record. STAT GR5999 serves as the course through which students admitted to the MA Research Specialization fulfill their research requirements.

Prerequisites: STAT G6201 and STAT G6201 This course will mainly focus on nonparametric methods in statistics. A tentavie list of topics to be covered include nonparametric density and regression function estimation -- upper bounds on the risk of kernel estimators and matching lower bounds on the minimax risk, reproducing kernel Hilbert spaces, bootstrap and resampling methods, multiple hypothesis testing, and high dimensional stastistical analysis.

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

Independent Study with Faculty Advisor must be registered for every semester after first academic year

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Departmental colloquium in probability theory.

A colloquiim in applied probability and risk.

A colloquium on topics in mathematical finance