Interdisciplinary PhD in Statistics

Tentative credit breakdown

- Stats: 35-40%

- Math: 25-30%

- CS: 20-25%

- Physics, Engineering: 10-15%

1st year classes

 500-level classes are Advanced undergraduate classes

Fall '19

* Physics 208 Calculus Physics II: Electromagnetism, waves, optics, relativity & quantum physics [Accelerated class, 2-course equivalent]

* Math 521 Real Analysis I: 1st half of Rudin's book

* CS 200 Java programming I - as audience

Spring '20

* Math 341 Linear Algebra: Proof-based, accelerated Honors

* Math 522 Analysis II: Approximation Theory & Fourier Analysis, Differential Calculus on Banach spaces, Functional Analysis

* Math 551 Elementary Topology: 1st half of Munkres' book

Summer '20

* ISYE 524 Intro to Discrete & Continuous Optimization: Comprehensive intro to all optimization types (LP,  Convex, Conic, IP, Stochastic)

* CS 540 Intro to Artificial Intelligence: Study of main ML algorithms, with emphasis on NN, SVM, Decision Tree, CNN, NLP, Game Theory

2nd year classes

600-level classes are Graduate-level classes (Master's / PhD), and 700-level (and above) classes are generally intended for PhD students exclusively

Fall '20

* Math 513 & 514 Numerical Linear Algebra & Numerical Analysis: Most chapters in Suli & Mayers

* Stats 609 Mathematical Statistics I: Grimmett & Stirzaker + Bickel & Docksum

* Stats 849 Regression and ANOVA I

* Physics 415 Thermodynamics and Statistical Mechanics - as audience

Spring '21

* Math 629 Intro to Lebesgue Measure Theory & Integration: Folland

* Stats 610 Statistical Inference I: Casella & Berger

* Stats 850 Regression and ANOVA II: Box, Hunter & Hunter

3rd year classes

Fall '21

* Stats 709 (Measure-theoretic) Mathematical Statistics II: Shao, Billingsley

* Math 733 (Measure-theoretic) Probability Theory I: Durrett

Spring '22

* Stats 710 (Measure-theoretic) Mathematical Statistics III: Shao

* Stats 775 (Measure-theoretic) Bayesian Statistics: relevant scientific papers

4th year classes

Fall '22

* Stats 679 Data Visualization: Wilke

* Stats 992 Topics in Machine Learning: relevant scientific papers

* Stats 998 Statistical Consulting

Spring '23

* CS 760 Machine Learning (algorithms focus): Prof. Kandasamy's lecture notes (ML PhD @ CMU), Andrew Ng's CS 229 notes

* ECE 761 Mathematical Machine Learning: Prof. Nowak's lecture notes + Shai*(SS, BD)

Summer '23

* CS 577 Algorithms - as audience

5th year classes

Fall '23

* Stats 771 Statistical Computing: Givens & Hoeting, Lange

* Stats 861 Mathematical Machine Learning II: Shai*(SS, BD), Mohri

Spring'24

* Stats 761 Decision Trees for Multivariate Analysis: Prof. Loh's research papers

+ auditing Stats 992 Interpretable and Explainable ML, with Prof. Kris Sankaran (Spring 2025)