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  5. 2026 Spring

Graduate Studies

2026 - Spring Semester

Disclaimer: Be advised that some information on this page may not be current due to course scheduling changes. Please view either the or your Class Schedule via for the most current/updated information..

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GRADUATE COURSES - SPRING 2026

 

SENIOR UNDERGRADUATE COURSES

Course/Section

Class #

Course Title

Course Day/Time

Rm #

Instructor 
Math 4309 11808 Mathematical Biology  MW, 2:30—4PM SEC 203 R. Azevedo
20112 Graph Theory w/Applications TTh, 4—5:30PM CBB 118 K. Josic
14554
Introduction to Data Science and Machine Learning
TTh, 11:30AM—1PM
SEC 204
C. Poliak

Math 4322 
25569
Introduction to Data Science and Machine Learning
MW, 1—2:30PM
SEC 204
 Y. Niu

Math 4322 
25569
Introduction to Data Science and Machine Learning
MW, 1—2:30PM
SEC 204
 Y. Niu
14124
Data Science and Statistical Learning
MW, 1—2:30PM
SEC 105
 W. Wang
Math 4332/6313
10770
Introduction to Real Analysis II 
MWF, 9—10AM
S 114
 A. Vershynina
20532
Partial Differential Equations I 
Asynch./on-campus
exams
Online
 J. Morgan
20113 Mathematics of Signal Representation MWF, 12PM—1PM SEC 203  D. Labate
13669
Theory of Differential Equations and Nonlinear Dynamics 
TTh, 11:30AM—1PM
CBB 106
 W. Ott
12558
Intro. to Numerical Analysis in Scientific Computing
MW, 4—5:30PM
 CBB 110
T.W. Pan
Math 4364-02
16276
Intro. to Numerical Analysis in Scientific Computing
TTh, 10—11:30AM
CEMO 105
 L. Cappanera
12145
Numerical Methods for Differential Equations
TTh, 10—11:30AM
C 135
 J. He
18670
Mathematics for Physicists
MW, 4—5:30PM
S 102
 A.Weglein
Math 4377/6308 
12357
Advanced Linear Algebra I
TTh, 11:30AM —1PM
SEC 205
 P. Zhong
Math 4378/6309 
10771
Advanced Linear Algebra II
TTh, 11:30AM —1PM
SW 229
 A. Torok
10772
A Mathematical Introduction to Options
TTh, 8:30—10AM
GAR G201
I. Timofeyev
10773
Survey of Undergraduate Mathematics
TTh, 10—11:30AM
CBB 106
 D. Blecher

 

GRADUATE ONLINE COURSES

Course/Section

 Class # 

Course Title

 Course Day & Time 

 Instructor 
Math 5330 11208 Abstract Algebra (Asynch.  online)   A. Haynes
Math 5332 10780 Differential Equations (Asynch.  online)   G. Etgen
Math 5334 20200 Complex Analysis (Asynch.  online)   S. Ji
Math 5385 18615 Statistics (Asynch.  online)   H. Jeon 
Math 5397 20533 Partial Differential Equations (Asynch.  online)   J. Morgan 

 

GRADUATE COURSES

Course/Section

Class #

Course Title

Course Day & Time

Rm#

Instructor
10781
Modern Algebra II
 TTh, 10—11:30AM SW 219 G. Heier
Math 6308 12358 Advanced Linear Algebra I TTh, 11:30AM—1PM SEC 205 P. Zhong
Math 6309  11247 Advanced Linear Algebra II TTh 11:30AM—1PM SW 229 A. Torok
Math 6313 11246 Introduction to Real Analysis MWF, 9—10AM S 114 A. Vershynina
Math 6321 10786 Functions Real Variable TTh, 1—2:30PM SW 219 D. Blecher
Math 6324 20114 Differential Equations MWF,  9—10AM F 154 V. Climenhaga
Math 6360 20115 Applicable Analysis TTh, 4—5:30PM CBB 108 A. Mamonov
Math 6367 17410 Optimization Theory TTh, 2:30—4PM MH 116 N. Charon
Math 6371  10787 Numerical Analysis TTh, 11:30AM—1PM  MH 120 Y. He
Math 6374 20116 Numerical Partial Differential Equations TTh, 8:30—10AM S 114 C. Puelz
Math 6377  20117 Mathematics of Machine Learning MW, 4—5:30PM SW 231 R. Azencott
Math 6383 10788 Statistics MW, 1—2:30PM MH 127 M. Jun
Math 6397 20206 Random Matrix Free Probability TTh, 8:30—10AM SW 219 P. Zhong
Math 6397 20207 Computational Science with C++ TTh, 8:30—10AM SEC 205 L. Cappanera
Math 6397 20236 Bayesian Statistics MW, 2:30—4PM SEC 205 Y. Niu
Math 6XXX TBD TBD TBD TBD TBD

 

MSDS Courses (MSDS Students Only)

(MSDS Students Only - Contact Ms. Tierra Kirts for specific class numbers)

Course/Section

Class #

Course Title

Course Day & Time

Rm#

Instructor
Math 6359 Not shown to students Applied Statistics & Multivariate Analysis  F, 1—3PM  CBB 108  C. Poliak
Math 6359 Not shown to students Applied Statistics & Multivariate Analysis  F, 1—3PM (synch. online)  Online  C. Poliak
Math 6373 Not shown to students Deep Learning and Artificial Neural Networks  MW, 1—2:30PM (F2F) SEC 206  D. Labate
Math 6381 Not shown to students Information Visualization  F, 3—5PM CBB 108  D. Shastri
Math 6381 Not shown to students Information Visualization  F, 1—3PM (synch. online)  Online  D. Shastri
Math 6397 Not shown to students Case Studies In Data Analysis  W, 5:30—8:30PM SEC 204  L. Arregoces
Math 6397 Not shown to students Financial & Commodity Markets  W, 5:30—8:30PM SEC 206  J. Ryan
Math 6397 Not shown to students Bayesian Statistics  MW, 2:30—4PM SEC 205  Y. Niu

 

SENIOR UNDERGRADUATE COURSES

MATH 4309  - Mathematical Biology
Prerequisites:
 and  or consent of instructor.
Text(s):
Required texts: A Biologist's Guide to Mathematical Modeling in Ecology and Evolution, Sarah P. Otto and Troy Day; (2007, Princeton University Press)
ISBN-13:9780691123448
Reference texts: (excerpts will be provided)
  • An Introduction to Systems Biology, 2/e, U. Alon (an excellent, recently updated text on the “design principles of biological circuits”)
  • Random Walks in Biology, H.C. Berg (a classic introduction to the applicability of diffusive processes and the Reynolds number at the cellular scale)
  • Mathematical Models in Biology, L. Edelstein-Keshet (a systematic development of discrete, continuous, and spatially distributed biological models)
  • Nonlinear Dynamics and Chaos, S.H. Strogatz (a very readable introduction to phase-plane analysis and bifurcation theory in dynamical systems with an emphasis on visual thinking; contains numerous applications in biology)
  • Thinking in Systems, D.H. Meadows (a lay introduction to control systems and analyzing parts-to-whole relationships, their organizational principles, and sensitivity in their design)
  • Adaptive Control Processes: A Guided Tour, R. Bellman (a classic, more technical introduction to self-regulating systems, feedback control, decision processes, and dynamic programming)
Description:
Catalog description: Topics in mathematical biology, epidemiology, population models, models of genetics and evolution, network theory, pattern formation, and neuroscience. Students may not receive credit for both MATH 4309 and BIOL 4309.

Instructor's description: An introduction to mathematical methods for modeling biological dynamical systems. This course will survey canonical models of biological systems using the mathematics of calculus, differential equations, logic, matrix theory, and probability.

Applications will span several spatial orders-of-magnitude, from the microscopic (sub-cellular), to the mesoscopic (multi-cellular tissue and organism) and macroscopic (population-level: ecological, and epidemiological) scales. Specific applications will include biological-signaling diffusion, enzyme kinetics, genetic feedback networks, population dynamics, neuroscience, and the dynamics of infectious diseases. Optional topics (depending on schedule and student interest) may be chosen from such topics as: game theory, artificial intelligence and learning, language processing, economic multi-agent modeling, Turing systems, information theory, and stochastic simulations.

The course will be taught from two complementary perspectives:
  1. critical analysis of biological systems’ modeling using applicable mathematical tools, and
  2. a deeper understanding of mathematical theory, illustrated through biological applications.
Relevant mathematical theory for each course section will be reviewed from first principles, with an emphasis on bridging abstract formulations to practical modeling techniques and dynamical behavior prediction.

The course will include some programming assignments, to be completed in Matlab or Python programming languages (available free through UH Software and public domain, respectively). However, advanced programming techniques are not required, and resources for introduction to these languages will be provided.

 

MATH 4315 - Graph Theory with Applications
Prerequisites: MATH 2305 or MATH 3325, and three additional hours at the MATH 3000-4000 level
Text(s): TBD
Description:
Introduction to basic concepts, results, methods, and applications of graph theory.
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MATH 4322 - Introduction to Data Science and Machine Learning
Prerequisites: MATH 3339
Text(s): Intro to Statistical Learning, Gareth James, 9781461471370
Description:

Theory and applications for such statistical learning techniques as linear and logistic regression, classification and regression trees, random forests, neutral networks. Other topics might include: fit quality assessment, model validation, resampling methods. R Statistical programming will be used throughout the course.

 

Math 4322 - Introduction to Data Science and Machine Learning
Prerequisites:
 MATH 3339
Text(s):
 Intro to Statistical Learning, Gareth James, 9781461471370
Description:
 Theory and applications for such statistical learning techniques as linear and logistic regression, classification and regression trees, random forests, neutral networks. Other topics might include: fit quality assessment, model validation, resampling methods. R Statistical programming will be used throughout the course.

 

MATH 4323 - Data Science and Statistical Learning
Prerequisites:
MATH 3339
Text(s):
Intro to Statistical Learning, Gareth James, 9781461471370
Description:
Theory and applications for such statistical learning techniques as maximal marginal classifiers, support vector machines, K-means and hierarchical clustering. Other topics might include: algorithm performance evaluation, cluster validation, data scaling, resampling methods. R Statistical programming will be used throughout the course.
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MATH 4332/6313 - Introduction to Real Analysis II
Prerequisites: MATH  or consent of instructor
Text(s): Real Analysis with Real Applications | Edition: 1; Allan P. Donsig, Allan P. Donsig; ISBN: 9780130416476
Description:

Further development and applications of concepts from MATH 4331. Topics may vary depending on the instructor's choice. Possibilities include: Fourier series, point-set topology, measure theory, function spaces, and/or dynamical systems.

 

MATH 4335 - Partial Differential Equations I 
Prerequisites: MATH 3331, or equivalent, and three additional hours of 3000-4000 level Mathematics.
Text(s): TBD
Description: Initial and boundary value problems, waves and diffusions, reflections, boundary values, Fourier series.

 

MATH 4355 -  Mathematics of Signal Representation
Prerequisites MATH 2415 and six additional hours of 3000-4000 level Mathematics.
Text(s): TBD
Description: Fourier series of real-valued functions, the integral Fourier transform, time-invariant linear systems, band-limited and time-limited signals, filtering and its connection with Fourier inversion, Shannon’s sampling theorem, discrete and fast Fourier transforms, relationship with signal processing.
 
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MATH 4362 - Theory of Differential Equations an Nonlinear Dynamics
Prerequisites: MATH 3331, or equivalent, and three additional hours of 3000-4000 level Mathematics. 
Text(s): Nonlinear Dynamics and Chaos (2nd Ed.) by Strogatz. ISBN: 978-0813349107
Description:

ODEs as models for systems in biology, physics, and elsewhere; existence and uniqueness of solutions; linear theory; stability of solutions; bifurcations in parameter space; applications to oscillators and classical mechanics.
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Math 4364 (xxxx) - Introduction to Numerical Analysis in Scientific Computing
Prerequisites:
MATH and COSC 1410 or equivalent or consent of instructor.
Instructor's Prerequisite Notes: 
1. MATH 2331, In depth knowledge of Math 3331 (Differential Equations) or Math 3321 (Engineering Mathematics)
2. Ability to do computer assignments in FORTRAN, C, Matlab, Pascal, Mathematica or Maple.
Text(s):
Instructor's notes
Description:
Catalog Description: Root finding, interpolation and approximation, numerical differentiation and integration, numerical linear algebra, numerical methods for differential equations.
Instructor's Description: This is an one semester course which introduces core areas of numerical analysis and scientific computing along with basic themes such as solving nonlinear equations, interpolation and splines fitting, curve fitting, numerical differentiation and integration, initial value problems of ordinary differential equations, direct methods for solving linear systems of equations, and finite-difference approximation to a two-points boundary value problem. This is an introductory course and will be a mix of mathematics and computing.
 
 
MATH 4364 - Introduction to Numerical Analysis in Scientific Computing
Prerequisites:
MATH and COSC 1410 or equivalent or consent of instructor.
Instructor's Prerequisite Notes: 
1. MATH 2331, In depth knowledge of Math 3331 (Differential Equations) or Math 3321 (Engineering Mathematics)
2. Ability to do computer assignments in FORTRAN, C, Matlab, Pascal, Mathematica or Maple.
Text(s):
Numerical Analysis (9th edition), by R.L. Burden and J.D. Faires, Brooks-Cole Publishers, ISBN:9780538733519
Description:
Catalog Description: Root finding, interpolation and approximation, numerical differentiation and integration, numerical linear algebra, numerical methods for differential equations.
Instructor's Description: This is an one semester course which introduces core areas of numerical analysis and scientific computing along with basic themes such as solving nonlinear equations, interpolation and splines fitting, curve fitting, numerical differentiation and integration, initial value problems of ordinary differential equations, direct methods for solving linear systems of equations, and finite-difference approximation to a two-points boundary value problem. This is an introductory course and will be a mix of mathematics and computing.
 
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MATH 4365 - Numerical Methods for Differential Equations
Prerequisites: MATH , or equivalent, and three additional hours of 3000–4000 level Mathematics.
Text(s): TBA
Description: Numerical differentiation and integration, multi-step and Runge-Kutta methods for ODEs, finite difference and finite element methods for PDEs, iterative methods for linear algebraic systems and eigenvalue computation.
 

 

MATH 4370 - Mathematics for Physicists
Prerequisites: MATH 2415, and MATH 3321 or MATH 3331
Text(s): TBD
Description: Vector calculus, tensor analysis, partial differential equations, boundary value problems, series solutions to differential equations, and special functions as applied to junior-senior level physics courses.
 
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Math 4377/6308 - Advanced Linear Algebra I
Prerequisites:
MATH or equivalent, and three additional hours of 3000–4000 level Mathematics.
Text(s):
Linear Algebra | Edition: 4; Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence; ISBN: 9780130084514
Description:
Linear systems of equations, matrices, determinants, vector spaces and linear transformations, eigenvalues and eigenvectors.
Additional Notes: This is a proof-based course. It will cover Chapters 1-4 and the first two sections of Chapter 5. Topics include systems of linear equations, vector spaces and linear transformations (developed axiomatically), matrices, determinants, eigenvectors and diagonalization.
 
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MATH 4378/6309 - Advanced Linear Algebra II
Prerequisites:
Text(s): Linear Algebra, Fourth Edition, by S.H. Friedberg, A.J Insel, L.E. Spence,Prentice Hall, ISBN 0-13-008451-4; 9780130084514
Description:
Similarity of matrices, diagonalization, Hermitian and positive definite matrices, normal matrices, and canonical forms, with applications.
Instructor's Additional notes: This is the second semester of Advanced Linear Algebra. I plan to cover Chapters 5, 6, and 7 of textbook. These chapters cover Eigenvalues, Eigenvectors, Diagonalization, Cayley-Hamilton Theorem, Inner Product spaces, Gram-Schmidt, Normal Operators (in finite dimensions), Unitary and Orthogonal operators, the Singular Value Decomposition, Bilinear and Quadratic forms, Special Relativity (optional), Jordan Canonical form.
 
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MATH 4380 - A Mathematical Introduction to Options
Prerequisites:   and . 
Text(s):  An Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation | Edition: 1; Desmond  Higham; 9780521547574
Description:  Arbitrage-free pricing, stock price dynamics, call-put parity, Black-Scholes formula, hedging, pricing of European and American options.
 

 

MATH 4389 - Survey of Undergraduate Mathematics
Prerequisites:
 , , , and three hours of 4000-level Mathematics.
Text(s):
 Instructor notes
Description:
 A review of some of the most important topics in the undergraduate mathematics curriculum.
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ONLINE GRADUATE COURSES

MATH 5330 - Abstract Algebra
Prerequisites: Graduate standing. 
Text(s):

Abstract Algebra , A First Course by Dan Saracino. Waveland Press, Inc. ISBN 0-88133-665-3
(You can use the first edition. The second edition contains additional chapters that cannot be covered in this course.)
Description:

Groups, rings and fields; algebra of polynomials, Euclidean rings and principal ideal domains. Does not apply toward the Master of Science in Mathematics or Applied Mathematics. 

Other Notes: This course is meant for  students who wish to pursue a Master of Arts in Mathematics (MAM). Please contact me  in order to find out whether this course is suitable for you and/or your degree plan. Notice that this course cannot be used for  MATH 3330, Abstract Algebra. 
 
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MATH 5332 - Differential Equations
Prerequisites: Graduate standing. .
Text(s): The text material is posted on Blackboard Learn, under "Content".
Description:
First-order equations, existence and uniqueness theory; second and higher order linear equations; Laplace transforms; systems of linear equations; series solutions. Theory and applications emphasized throughout. Applies toward the Master of Arts in Mathematics degree; does not apply toward the Master of Science in Mathematics or the Master of Science in Applied Mathematics degrees.
 

 

MATH 5334 - Complex Analysis
Prerequisites:
Graduate standing. MATH 5333 or consent of instructor.
Text(s):
TBD
Description:
 Complex numbers, holomorphic functions, linear transformations, Cauchy integral theorem and residue theorem.
 
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MATH 5385 - Statistics
Prerequisites:
Graduate standing, Two semesters of calculus and one semester of linear algebra or consent of instructor.
Text(s):
TBD
Description:
Data collection and types of data, descriptive statistics, probability, estimation, model assessment, regression, analysis of categorical data, analysis of variance. Computing assignments using a prescribed software package (e.g., R or Matlab) will be given. Applies toward the Master of Arts in Mathematics degree; does not apply toward Master of Science in Mathematics or the Master of Science in Applied Mathematics degrees.
 

 

MATH 5397 - Partial Differential Equations
Prerequisites: Graduate standing
Text(s): TBD
Description:  

 

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GRADUATE COURSES  

MATH 6303 - Modern Algebra II
Prerequisites:
Graduate standing. MATH 4333 or MATH 4378 
Additional Prerequisites: students should be comfortable with basic measure theory, groups rings and fields, and point-set topology
Text(s):
No textbook is required.
Description:
Topics from the theory of groups, rings, fields, and modules. 
Additional Description: This is primarily a course about analysis on topological groups. The aim is to explain how many of the techniques from classical and harmonic analysis can be extended to the setting of locally compact groups (i.e. groups possessing a locally compact topology which is compatible with their algebraic structure). In the first part of the course we will review basic point set topology and introduce the concept of a topological group. The examples of p-adic numbers and the Adeles will be presented in detail, and we will also spend some time discussing SL_2(R). Next we will talk about characters on topological groups, Pontryagin duality, Haar measure, the Fourier transform, and the inversion formula. We will focus on developing details in specific groups (including those mentioned above), and applications to ergodic theory and to number theory will be discussed.
 
{back to Graduate Courses}
MATH 6308 - Advanced Linear Algebra I
Prerequisites: Graduate standing. MATH  and a minimum of 3 semester hours transformations, eigenvalues and eigenvectors.
Text(s): Linear Algebra | Edition: 4; Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence; ISBN: 9780130084514
Description:

Transformations, eigenvalues and eigenvectors.

Additional Notes: This is a proof-based course. It will cover Chapters 1-4 and the first two sections of Chapter 5. Topics include systems of linear equations, vector spaces and linear transformations (developed axiomatically), matrices, determinants, eigenvectors and diagonalization.
 

 

MATH 6309 - Advanced Linear Algebra II 
Prerequisites: Graduate standing and 
Text(s): Linear Algebra, Fourth Edition, by S.H. Friedberg, A.J Insel, L.E. Spence,Prentice Hall, ISBN 0-13-008451-4; 9780130084514
Description: Similarity of matrices, diagonalization, hermitian and positive definite matrices, canonical forms, normal matrices, applications. An expository paper or talk on a subject related to the course content is required. 
 
 
MATH 6313 - Introduction to Real Analysis II
Prerequisites: Graduate standing and .
Text(s): Kenneth Davidson and Allan Donsig, “Real Analysis with Applications: Theory in Practice”, Springer, 2010; or (out of print) Kenneth Davidson and Allan Donsig, “Real Analysis with Real Applications”, Prentice Hall, 2001.
Description: Properties of continuous functions, partial differentiation, line integrals, improper integrals, infinite series, and Stieltjes integrals. An expository paper or talk on a subject related to the course content is required.
 
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MATH 6321 - Theory of Functions of a Real Variable
Prerequisites:

Graduate standing. MATH 4332 or consent of instructor.

Instructor's Prerequisite Notes: MATH 6320
Text(s):

Primary (Required): , Richard F. Bass

Supplementary (Recommended): Real Analysis: Modern Techniques and Their Applications, Gerald Folland (2nd edition); ISBN: 9780471317166.
Description:

Lebesque measure and integration, differentiation of real functions, functions of bounded variation, absolute continuity, the classical Lp spaces, general measure theory, and elementary topics in functional analysis. 

Instructor's Additional Notes: Math 6321 is the second course in a two-semester sequence intended to introduce the theory and techniques of modern analysis. The core of the course covers elements of functional analysis, Radon measures, elements of harmonic analysis, the Fourier transform, distribution theory, and Sobolev spaces. Additonal topics will be drawn from potential theory, ergodic theory, and the calculus of variations.
 

 

MATH 6324 - Differential Equations
Prerequisites: Graduate Standing. MATH 4331
Text(s): TBD
Description: General theories, topics in ordinary and partial differential equations, and boundary value problems.
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MATH 6359 - Applied Statistics & Multivariate Analysis
Prerequisites:

Graduate standing. MATH 3334, MATH 3338 or MATH 3339, and MATH 4378. Students must be in the Statistics and Data Science, MS Program
Text(s):

While lecture notes will serve as the main source of material for the course, the following book constitutes a great reference:

- ”Statistics and Data Analysis from Elementary to Intermediate” by Tamhane, Ajit and Dunlop, Dorothy ISBN: 0137444265
- ”Applied Multivariate Statistics with R”, by Daniel Zelterman, ISBN: 9783319140926
- ”Applied Multivariate Statistical Analysis, sixth edition”, by Richard A. Johnson and Dean W. Wichern, published by Pearson.
- Rstudio: Make sure to download R and RStudio (which can’t be installed without R) before the course starts. Use the link R download to download R frst, then RStudio download to download it from the mirror appropriate for your platform.
Description:

Linear models, loglinear models, hypothesis testing, sampling, modeling and testing of multivariate data, dimension reduction.

< Course syllabus >
 
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MATH 6360 - Applicable Analysis
Prerequisites: Graduate Standing. 
Text(s): TBD
Description: Solvability of finite dimensional, integral, differential, and operator equations, contraction mapping principle, theory of integration, Hilbert and Banach spaces, and calculus of variations
 

 

MATH 6367 - Optimization Theory
Prerequisites: Graduate standing. MATH 4331 and MATH 4377.
Text(s):
  •  D.P. Bertsekas; Dynamic Programming and Optimal Con- trol, Vol. I, 4th Edition. Athena Scientific, 2017, ISBN-10: 1-886529-43-4
  • J.R. Birge and F.V. Louveaux; Introduction to Stochastic Programming. Springer, New York, 1997, ISBN: 0-387-98217-
Description:

Constrained and unconstrained finite dimensional nonlinear programming, optimization and Euler-Lagrange equations, duality, and numerical methods. Optimization in Hilbert spaces and variational problems. Euler-Lagrange equations and theory of the second variation. Application to integral and differential equations.

Additional Description: This course consists of two parts. The first part is concer- ned with an introduction to Stochastic Linear Programming (SLP) and Dynamic Programming (DP). As far as DP is concerned, the course focuses on the theory and the appli- cation of control problems for linear and nonlinear dynamic systems both in a deterministic and in a stochastic frame- work. Applications aim at decision problems in finance. In the second part, we deal with continuous-time systems and optimal control problems in function space with em- phasis on evolution equations.
 
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MATH 6371 - Numerical Analysis
Prerequisites: Graduate standing.
Text(s): Numerical Mathematics (Texts in Applied Mathematics), 2nd Ed., V.37, Springer, 2010. By A. Quarteroni, R. Sacco, F. Saleri. ISBN: 9783642071010
Description: Ability to do computer assignments. Topics selected from numerical linear algebra, nonlinear equations and optimization, interpolation and approximation, numerical differentiation and integration, numerical solution of ordinary and partial differential equations. 
 

 

MATH 6373 - Deep Learning and Artificial Neural Networks
Prerequisites: Graduate standing. Probability/Statistic and linear algebra or consent of instructor. Students must be in Master’s in Statistics and Data Science program.
Text(s): TBD
Description: Artificial neural networks for automatic classification and prediction. Training and testing of multi-layers perceptrons. Basic Deep Learning methods. Applications to real data will be studied via multiple projects.
 
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MATH 6374 - Numerical Partial Differential Equations
Prerequisites: Graduate Standing. MATH 6371
Text(s): TBD
Description: Finite difference, finite element, collocation and spectral methods for solving linear and nonlinear elliptic, parabolic, and hyperbolic equations and systems with applications to specific problems.

 

MATH 6377- Mathematics of Machine Learning
Prerequisites: Graduate Standing. Linear Algebra, Real Analysis (MATH 4331-4332), Probability.
Text(s): TBD
Description: This course is an introduction to the theoretical foundations of machine learning and is focused on the underlying mathematical concepts needed to understand the methods used in modern data science, without neglecting relevant algorithmic and computational aspects of the subject. Examples of covered topics might include - Support Vector Machines, Reproducing Kernel Hilbert Spaces, the Vapnik-Chervonenkis theory, concentration inequalities, dimensionality reduction and spectral clustering. This class is designed for graduate students interested in mastering theoretical tools underlying machine learning and data science

 

MATH 6381 - Information Visualization
Prerequisites: Graduate standing. MATH 6320 or consent of instructor. 
Text(s):  TBD
Description: Random variables, conditional expectation, weak and strong laws of large numbers, central limit theorem, Kolmogorov extension theorem, martingales, separable processes, and Brownian motion.
 
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MATH 6383 - Statistics
Prerequisites: Graduate standing. MATH 6382 or consent of instructor.
Text(s):

 TBD
Description:  A survey of statistics. Includes statistical inference using parametric and nonparametric methods.

 

MATH 6397 (20206)  - Random Matrix Free Probability
Prerequisites:
 Graduate standing
 Text(s):
TBD
 Description:
 TBD
{back to Graduate Courses}
 
MATH 6397 (20207) - Computational Science with C++
Prerequisites: Graduate standing. 
Text(s):
 TBD
Description:
 TBD

 

MATH 6397 (20236) - Bayesian Statistics
Prerequisites: Graduate standing. Graduate Probability.
Text(s):
  • Peter Hoff (2009). A first course in Bayesian statistical methods. Springer
  • Brian J. Reich & Sujit K. Ghosh (2019). Bayesian Statistical Methods. CRC Press.
  • Christian P. Robert (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation. Springer (2nd Edition).
Description:

This is an introductory course on Bayesian statistics for graduate students. The course introduces the Bayesian paradigm and focus on Bayesian modeling, computation, and inference.

  • We first convey the ideology of Bayesian statistics which is a particular approach to statistical inference that differs philosophically and operationally from the classic frequentist approach.
  • We then define Bayesian inference and discuss its advantages. Detailed applications are illustrated using some classical models, including binomial, Poisson, univariate normal, multivariate normal model, and linear regression.
  • We go through each step of building Bayesian hierarchical models and apply Bayes’ theorem to derive posterior distributions.
  • To inference on posterior distributions, MCMC algorithm is introduced as a modern method of approximating posteriors
{back to Graduate Courses} 
 
MATH 6397 - Case Studies In Data Analysis
Prerequisites:  Graduate standing.
Text(s):
 TBD
Description:
TBD
 
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MATH 6397 - Financial & Commodity Markets
Prerequisites:  Graduate standing. 
Text(s):
 TBD
Description:
 TBD

 

MATH 6397 - Bayesian Statistics
Prerequisites:  Graduate standing. 
Text(s):
 TBD
Description:
 TBD
 
 
MATH 6397  - TBD
Prerequisites:  Graduate standing. 
Text(s):
 TBD
Description:
 TBD
 
{back to MSDS Courses}
MATH 7XXX - TBD
Prerequisites: Graduate standing. 
Text(s):
TBD
Description:
TBD
 
{back to Graduate Courses}
MATH 7XXX - TBD
Prerequisites: Graduate standing. 
Text(s): TBD
Description:
TBD
 
 
MATH 7XXX - TBD
Prerequisites: Graduate standing.
Text(s): TBD
Description: Catalog Description: TBD

Instructor's Description: TBD
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(Updated 11/19/25)