Program Overview

Introduction to the Statistics/Machine Learning Joint Ph.D. Degree

The Statistics/Machine Learning Joint Ph.D. Degree is a unique program that blends the power of statistics with cutting-edge machine learning, preparing students to tackle complex challenges at the intersection of both fields. By engaging in interdisciplinary research and coursework across two dynamic departments, students gain a rich, dual perspective that equips them to drive innovation in data science.

Program Overview

• The program offers a rare opportunity to push the boundaries of statistical and machine learning knowledge.
• Graduates leave with the skills and expertise to lead in academia, industry, or beyond.
• The program is designed for students who are interested in pursuing a career in data science, with a focus on statistical and machine learning methods.

Program Requirements

• Students must complete the Ph.D. Core Requirements as well as the Joint Ph.D. requirements from the ML department.
• The Data Analysis Exam is not required for this joint Ph.D. program, but it is necessary for obtaining the M.S. in Statistics.

Year 1

• Fall:
  • 36-705: Intermediate Statistics
  • 36-707: Applied Regression
  • 36-750: Statistical Computing
  • 36-699: Statistical Immigration
• Spring:
  • 36-709: Advanced Statistical Theory I
  • 36-708: Statistical Machine Learning
  • 36-757: Advanced Data Analysis I

Year 2

• Fall:
  • 10-715: Advanced Introduction to Machine Learning
  • 36-758: Advanced Data Analysis II
• Spring:
  • 10-716: Advanced Machine Learning
  • 10-725: ML required elective, e.g., Convex Optimization
  • Research

Year 3

• Prepare and deliver your thesis proposal.

Year 4 and Beyond

• Dedicated to dissertation research.

Course Descriptions

Year 1 - Fall

• 36-699: Statistical Immigration
  • Students are introduced to the faculty and their interests, the field of statistics, and the facilities at Carnegie Mellon.
  • Each faculty member gives at least one elementary lecture on some topic of his or her choice.
• 36-700: Probability and Mathematical Statistics
  • This course covers the basics of statistics, including probability theory, point estimation, hypothesis testing, asymptotic theory, and Bayesian inference.
• 36-705: Intermediate Statistics
  • This course covers the fundamentals of theoretical statistics, including probability inequalities, point and interval estimation, minimax theory, hypothesis testing, data reduction, convergence concepts, Bayesian inference, nonparametric statistics, bootstrap resampling, VC dimension, prediction, and model selection.
• 36-707: Regression Analysis
  • This course covers the basic principles of causality, foundations of linear regression, including theory, computation, diagnostics, and generalized linear models.
• 36-750: Statistical Computing
  • A detailed introduction to elements of computing relating to statistical modeling, targeted to PhD students and masters students in Statistics & Data Science.

Year 1 - Spring

• 36-708: Statistical Machine Learning
  • This course focuses on statistical methods for machine learning, including algorithm design principles, bias-variance thinking, computational considerations, data analysis, and explainability and interpretability.
• 36-709: Advanced Statistics I
  • This is a core Ph.D. course in theoretical statistics, covering a selection of modern topics in mathematical statistics, focusing on high-dimensional parametric models and non-parametric models.
• 36-757: Advanced Data Analysis I
  • Advanced Data Analysis (ADA) is a Ph.D. level seminar on advanced methods in statistics, including computationally intensive smoothing, classification, variable selection, and simulation techniques.

Year 2 - Fall

• 10-715: Advanced Introduction to Machine Learning
  • Machine Learning is the primary pillar that Artificial Intelligence is built upon, giving students a thorough grounding in the algorithms, mathematics, theories, and insights needed to do in-depth research and applications in machine learning.
• 36-758: Advanced Data Analysis II
  • Advanced Data Analysis (ADA) is a Ph.D. level seminar on advanced methods in statistics, including computationally intensive smoothing, classification, variable selection, and simulation techniques.

Year 2 - Spring

• 10-716: Advanced Machine Learning
  • This course is for students who have already taken introductory courses in machine learning and statistics, and who are interested in deeper theoretical foundations of machine learning, as well as advanced methods and frameworks used in modern machine learning.
• 10-725: Convex Optimization
  • Nearly every problem in machine learning can be formulated as the optimization of some function, possibly under some set of constraints, covering the formulation and solution of convex optimization problems, though discussing some recent advances in nonconvex optimization.