STAT1010 - Intro Business Stat (Course Syllabus)
Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: MATH 1070 OR MATH 1400 OR MATH 1100
STAT1018 - Intro Business Stat (Course Syllabus)
The STAT 1018 honors section, which fulfills the STAT 1010 requirement offers an introduction to probability and statistics for students who have studied calculus and are seeking a class with mathematical content. The class will assume good command of the material in MATH 1400 as a prerequisite. Although multivariate calculus is not a formal prerequisite for the class concurrent enrollment in MATH 1410 or MATH 1610 may be a good choice for first or second year students who are interested in developing firm foundations for the more mathematical statistics classes. Stat 1018 is particularly recommended for those considering a statistics and data science concentration or minor.
Prerequisites: MATH 1400
STAT1020 - Intro Business Stat (Course Syllabus)
Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1010 OR STAT 1018
STAT1028 - Intro Business Stat (Course Syllabus)
The STAT 1028 honors section covers many of the same topics as STAT 1020 (such as multiple linear and logistic regression) and is designed for students who have studied calculus and are seeking a class with mathematical content. The class will assume good command of the material in MATH 1400 as a prerequisite. Although multivariable calculus is not a formal prerequisite, concurrent enrollment in MATH 1410 or MATH 1610 may be a good choice for first- or second-year students who are interested in developing firm foundations for the more mathematical statistics classes. STAT 1028 is particularly recommended for those considering a statistics and data science concentration or minor.
Prerequisites: STAT 1010 OR STAT 1018
STAT1110 - Introductory Statistics (Course Syllabus)
Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package. Knowledge of high school algebra is required for this course.
STAT1120 - Introductory Statistics (Course Syllabus)
Further development of the material in STAT 1110, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1110
STAT3990 - Independent Study (Course Syllabus)
Written permission of instructor and the department course coordinator required to enroll in this course.
STAT4010 - Sports Analytics (Course Syllabus)
This course would introduce undergraduate students to the growing field of sports analytics, while allowing them to implement and integrate their knowledge base by exploring real sports data sets to solve real problems. While the context will be sports related, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. Prerequisites: Must be a declared Statistics Concentrator or Business Analytics Concentrator or Statistics Minor or Data Science Minor. Permission from the Instructor is required. An interest in sports is highly recommended.
Prerequisites: WH 1010 AND WH 2010 AND MGMT 3010
STAT4020 - Communicating Quant. Analyses (Course Syllabus)
This seminar-based capstone course provides an opportunity for students to hone their data science and statistical modeling skills, together with an emphasis on communicating quantitative results. This is not a “theoretical class”, but rather, experiential. It allows students to bring their existing knowledge from different disciplines to bear on new problems. Four real-life datasets will be analyzed during the quarter, and students will be expected to create and deliver in-class presentations for each analysis. The course will be suitable for anyone who wants more opportunities to analyze data, continue developing their programming skills and those who want to gain experience and confidence in presenting results and conclusions to an audience. Prerequisites: The course presumes that students have taken a sequence of stat courses such as STAT 1010/1020, or 4300/4310 and so are familiar with multiple regression analysis. In addition, they should have been exposed to more advanced techniques such as logistic regression and tree-based methods as taught in classes like STAT 4220/4230/4710. Finally, it will be assumed that students have knowledge of a programming language such as R or Python and an IDE such as R-Studio or Jupyter notebooks. Classes such as STAT 4050/4700 would meet this requirement.
Prerequisites: (STAT 1010 OR STAT 1018 OR STAT 1020 OR STAT 1028 OR STAT 4300 OR STAT 4310) AND (STAT 4050 OR STAT 4700 OR STAT 4710) AND WH 1010 AND WH 2010 AND MGMT 3010
STAT4050 - Stat Computing with R (Course Syllabus)
The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4300
STAT4100 - Data Collect & Acquisit (Course Syllabus)
This course will give students a solid grasp of different data collection strategies and when and how they can be applied in practice. At the same time, important current ideas such as data confidentiality and ethical considerations will be addressed. The students will have designed and fielded a sample survey and designed and fielded an online experiment (A/B test). Student will collect data through web scraping activities and/or using an API. Students will summarize their collected data and subsequent inferences, culminating with an in-class presentation. The course is structured in two parts. The first part is a "Strategies" component that addresses different data collection strategies. It will discuss sample designs, experimentation, and observational studies. The second part of the course is about "Platforms" and goes into the practicalities of the implementation of the different strategies. Given the data science perspective of this course, this is focused on web enabled approaches. Familiarity with either R or Python is expected and specifically the R-Studio or Jupyter notebooks platforms. Courses such as Stat 4050 or Stat 4770 would meet this requirement. Statistics, through the level of multiple regression is required. This requirement may be fulfilled with undergraduate courses such as Stat 1020 or Stat 1120.
STAT4220 - Predictive Analytics (Course Syllabus)
This course follows from the introductory regression classes, STAT 1020, STAT 1120, and STAT 4310 for undergraduates and STAT 6130 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodologies known as random forest and boosted trees. By the end of the course the student will be familiar with and have applied these concepts and will be ready to use them in a work setting. The methodologies are implemented in a variety of software packages. Applications in JMP emphasize concepts and key modeling decisions. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310
STAT4230 - Machine Learning in Business (Course Syllabus)
This course introduces students to machine learning techniques used in business applications. The main topics include: cross validation, variable selection procedures, shrinkage methods such as lasso, logistic regression, k-nearest neighbors, ROC curves and confusion matrix, trees, kernel based learning, resampling techniques, random forests, boosting, neural networks & deep learning, matrix methods including singular value decomposition (SVD) and its application in principal component analysis (PCA), and some unsupervised methods such as k-means and density based clustering. Students will learn to apply these methods in a wide range of settings such as marketing and finance, and will gain hands-on experience through class assignments and competitions. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310
STAT4240 - Text Analytics (Course Syllabus)
This course introduces modern text analytics, and the tools of natural language processing. Text and language are powerful repositories of knowledge and information, but the semi-structured nature of language makes deriving insights from text challenging. Modern analytic techniques introduced in this course make it significantly easier even for non-specialists to use text and language data to drive deep insights. The course will use several examples from real world applications in different industries such as ecommerce, healthcare and finance to illustrate these techniques. Students should be familiar with regression models at the level of Stat 6130 or Stat 1020, and the Python language at the level of Stat 4770 or Stat 7770. Familiarity with the Jupyter notebook development environment is presumed, as well as common Python packages such as pandas, NLTK and SpaCy. Those with more knowledge of Statistics, such as from Stat 7220/4220, or computing skills will benefit. The predominant software used in the course is Jupyter notebooks that use a Python interpreter. Familiarity with basic probability models is helpful but not presumed.
STAT4300 - Probability (Course Syllabus)
Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.
Prerequisites: MATH 1080 OR MATH 1410 OR MATH 1510
STAT4310 - Statistical Inference (Course Syllabus)
Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 4300
STAT4320 - Mathematical Statistics (Course Syllabus)
An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 4300 OR STAT 5100
STAT4330 - Stochastic Processes (Course Syllabus)
An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.
Prerequisites: STAT 4300 AND (MATH 2400 OR MATH 3120 OR MATH 3140)
STAT4350 - Forecasting Methods Mgmt (Course Syllabus)
This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310
STAT4420 - Intro Bayes Data Analys (Course Syllabus)
The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.
STAT4700 - Data Analy & Stat Comp (Course Syllabus)
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: (STAT 1010 AND STAT 1020) OR (STAT 1110 AND STAT 1120) OR STAT 4310 OR (ECON 2300 AND ECON 2310)
STAT4710 - Modern Data Mining (Course Syllabus)
With the advent of the internet age, data are being collected at unprecedented scale in almost all realms of life, including business, science, politics, and healthcare. Data mining—the automated extraction of actionable insights from data—has revolutionized each of these realms in the 21st century. The objective of the course is to teach students the core data mining skills of exploratory data analysis, selecting an appropriate statistical methodology, applying the methodology to the data, and interpreting the results. The course will cover a variety of data mining methods including linear and logistic regression, penalized regression (including lasso and ridge regression), tree-based methods (including random forests and boosting), and deep learning. Students will learn the conceptual basis of these methods as well as how to apply them to real data using the programming language R. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310
STAT4730 - Data Science Using ChatGPT (Course Syllabus)
Large Language Models (LLMs) such as ChatGPTs are enabling data scientists to shift their focus from hands-on coding, data-wrangling, and conducting analyses to designing questions, assessing and managing analyses and translating results into decisions. Students can benefit by equipping themselves with practical data science skills that leverage artificial intelligence tools/large language models such as ChatGPT. This course will cover all aspects of a standard data science pipeline including Data Preparation and Cleaning, Visualization, Model building, Interpretation and Presentation.
STAT4740 - Modern Regression (Course Syllabus)
Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. Conceptual foundations are addressed as well as hands-on use for data analysis. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120
STAT4750 - Sample Survey Design (Course Syllabus)
This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 1020 OR STAT 1120 OR STAT 4310
STAT4760 - Appl Prob Models Mktg (Course Syllabus)
This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret powerful models of consumer behavior. Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techniques. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.
STAT4770 - Intro To Python Data Sci (Course Syllabus)
The goal of this course is to introduce the Python programming language within the context of the closely related areas of statistics and data science. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Competency in Python is a critical skill for students interested in data science. Prerequisites: No prior programming experience is expected, but statistics, through the level of multiple regression is required. This requirement may be fulfilled with Undergraduate courses such as Stat 1020, Stat 1120.
STAT4800 - Adv Stat Computing (Course Syllabus)
This course will build on the fundamental concepts introduced in the prerequisite courses to allow students to acquire knowledge and programming skills in large-scale data analysis, data visualization, and stochastic simulation. Prerequisite: STAT 7700 or 7050 or equivalent background acquired through a combination of online courses that teach the R language and practical experience. This course may be taken concurrently with the prerequisite with instructor permission.
Prerequisites: STAT 4050 OR STAT 4700
STAT4810 - Conv Optim Stat Data Sci (Course Syllabus)
Convex optimization has become a real pillar of modern data science and has transformed algorithm designs. A wide spectrum of problems in statistics, machine learning, and engineering can be formulated as optimization tasks that exhibit favorable convexity properties, which admit standardized and efficient solutions. This course aims to introduce the elements of convex optimization, concentrating on modeling aspects and algorithms that are useful in data science applications. Topics include convex sets, convex functions, linear and quadratic programs, semidefinite programming, optimality conditions and duality theory. We will visit important applications in statistics and machine learning to demonstrate the wide applicability of convex optimization. We will also cover effective optimization algorithms like gradient descent and Newton's method. Prerequisites: Basic linear algebra (Math 3120, 3130, 3140 or equivalent), basic calculus (Math 2400 or equivalent), basic probability (STAT 4300 or equivalent), and knowledge of a programming language like MATLAB or Python to conduct simulation exercises.
STAT4830 - Optimization for Data Sci & ML (Course Syllabus)
Optimization is the modeling language in which modern data science, machine learning, and sequential decision-making problems are formulated and solved numerically. This course will teach students how to formulate these problems mathematically, choose appropriate algorithms to solve them, and implement and tune the algorithms in the software PyTorch. PyTorch is a freely available machine learning library is recognized as one of the two most popular machine learning libraries alongside TensorFlow. By the end of the course, students will become an intelligent consumer of numerical methods and software for solving modern optimization problems.
STAT4900 - Causal Inference (Course Syllabus)
Questions about cause are at the heart of many everyday decisions and public policies. Does eating an egg every day cause people to live longer or shorter or have no effect? Do gun control laws cause more or less murders or have no effect? Causal inference is the subfield of statistics that considers how we should make inferences about such questions. This course will cover the key concepts and methods of causal inference rigorously. The course is intended for statistics concentrators and minors. Knowledge of R such as that covered in STAT 4050 or STAT 4700 is recommended.
Prerequisites: STAT 4300 AND (STAT 1020 OR STAT 1120 OR STAT 4310)
STAT5000 - Applied Reg & Analy Var (Course Syllabus)
An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT5010 - Int To Nonp & Loglin Mod (Course Syllabus)
An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.
STAT5030 - Data Analy & Stat Comp (Course Syllabus)
This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations (bootstrap, permutation tests). Prerequisite: Two courses at the statistics 4000 or 5000 level.
STAT5100 - Probability (Course Syllabus)
Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals. A one-year course in calculus is recommended.
STAT5110 - Statistical Inference (Course Syllabus)
Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course.
Prerequisites: STAT 5100
STAT5120 - Mathematical Statistics (Course Syllabus)
An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.
Prerequisites: STAT 4300 OR STAT 5100
STAT5150 - Adv Stat Inference I (Course Syllabus)
STAT 5150 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.
Prerequisites: STAT 4300 AND STAT 4310 AND MATH 2400
STAT5160 - Adv Stat Inference II (Course Syllabus)
STAT 5160 is a natural continuation of STAT 5150, and the main focus is on asymptotic evaluations and regression models. Time permitting, it also discusses some basic nonparametric statistical methods.
Prerequisites: STAT 5150
STAT5200 - Applied Econometrics I (Course Syllabus)
This is a course in econometrics for graduate students. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. Students taking the course should be familiar with elementary statistical methodology and basic linear algebra, and should have some programming experience. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models.
Prerequisites: (MATH 1080 OR MATH 1410) AND MATH 3120
STAT5210 - Applied Econometrics II (Course Syllabus)
Topics include system estimation with instrumental variables, fixed effects and random effects estimation, M-estimation, nonlinear regression, quantile regression, maximum likelihood estimation, generalized method of moments estimation, minimum distance estimation, and binary and multinomial response models. Both theory and applications will be stressed.
Prerequisites: STAT 5200
STAT5330 - Stochastic Processes (Course Syllabus)
An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.
Prerequisites: STAT 5100
STAT5350 - Forecasting Methods Mgmt (Course Syllabus)
This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.
STAT5420 - Bayesian Meth & Comp (Course Syllabus)
Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.
Prerequisites: STAT 4300 OR STAT 5100
STAT5440 - Applied Bayesian Modeling (Course Syllabus)
This is a 5000-level graduate course that focuses on the application of statistical techniques from a Bayesian perspective. It is designed for high-level senior undergraduate students who have completed course 4420, as well as for graduate students from various non-statistics fields interested in applying Bayesian methods to their research. The curriculum begins with a refresher on Bayesian statistical principles, followed by practical applications using established software like Stan for model sampling. Critical subjects included in the course are Bayesian model selection, Stan programming, BRMS, variational Bayes methods, regression and mixed effects models, hierarchical structures, dynamic linear models, survival analysis, Gaussian processes, and explorations in nonparametric Bayesian approaches.
STAT5710 - Modern Data Mining (Course Syllabus)
Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. Prerequisite: two courses at the statistics 4000 or 5000 level or permission from instructor.
STAT5800 - Adv Stat Computing (Course Syllabus)
This course will build on the fundamental concepts introduced in the prerequisite courses to allow students to acquire knowledge and programming skills in large-scale data analysis, data visualization, and stochastic simulation. Prerequisite: STAT 5030, 7050, or 7700 or equivalent background acquired through a combination of online courses that teach the R language and practical experience.
Prerequisites: STAT 5030 OR STAT 7050 OR STAT 7700
STAT5810 - Conv Optim Stat Data Sci (Course Syllabus)
Convex optimization has become a real pillar of modern data science and has transformed algorithm designs. A wide spectrum of problems in statistics, machine learning, and engineering can be formulated as optimization tasks that exhibit favorable convexity properties, which admit standardized and efficient solutions. This course aims to introduce the elements of convex optimization, concentrating on modeling aspects and algorithms that are useful in data science applications. Topics include convex sets, convex functions, linear and quadratic programs, semidefinite programming, optimality conditions and duality theory. We will visit important applications in statistics and machine learning to demonstrate the wide applicability of convex optimization. We will also cover effective optimization algorithms like gradient descent and Newton's method. Prerequisites: Basic linear algebra, basic calculus, basic probability, and knowledge of a programming language like MATLAB or Python to conduct simulation exercises.
STAT5900 - Causal Inference (Course Syllabus)
Questions about cause are at the heart of many everyday decisions and public policies. Does eating an egg every day cause people to live longer or shorter or have no effect? Do gun control laws cause more or less murders or have no effect? Causal inference is the subfield of statistics that considers how we should make inferences about such questions. This course will cover the key concepts and methods of causal inference rigorously. Background in probability and statistics; some knowledge of R is recommended.