Interdisciplinary PhD in Statistics
Special emphasis on Mathematics and Computer Science to support the development of Machine Learning theory.
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