• An Introduction to Stochastic Processes, by Edward P. C. Kao, Dover 2019, Duxbury Press, 1997; ISBN 9780486837925
• An Introduction to Probability with Mathematica, by Edward P. C. Kao, World Scientific, May 2022; ISBN: 9789811246784

Catalog Description: We study the theory and applications of stochastic processes. Topics include discrete-time and continuous-time Markov chains, Poisson process, branching process, Brownian motion. Considerable emphasis will be given to applications and examples.

Instructor's description: This course provides a overview of stochastic processes. We cover Poisson processes, discrete-time and continuous-time Markov chains, renewal processes, diffusion process and its variants, marttingales. We also study Markov chain Monte Carlo methods, and regenerative processes. In addition to covering basic theories, we also explore applications in various areas such as mathematical finance.

Syllabus can be found here: https://www.math.uh.edu/~edkao/MyWeb/doc/math4320_fall2022_syllabus.pdf

Instructor's notes. TBA

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

Learning Objectives: By the end of the course a successful student should:

• Have a solid conceptual grasp on the described statistical learning methods.
• Be able to correctly identify the appropriate techniques to deal with particular data sets.
• Have a working knowledge of R programming software in order to apply those techniques and subse- quently assess the quality of fitted models.
• Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data.

Software: Make sure to download R and RStudio (which can’t be installed without R) before the course starts. Use the link https://www.rstudio.com/products/rstudio/download/ to download it from the mirror appropriate for your platform. Let me know via email in case you encounter difficulties.

Course Outline:
Introduction: What is Statistical Learning?

Supervised and unsupervised learning. Regression and classification.
Linear and Logistic Regression. Continuous response: simple and multiple linear regression. Binary response: logistic regression. Assessing quality of fit.
Model Validation. Validation set approach. Cross-validation.
Tree-based Models. Decision and regression trees: splitting algorithm, tree pruning. Random forests: bootstrap, bagging, random splitting.
Neural Networks. Single-layer perceptron: neuron model, learning weights. Multi-Layer Perceptron: backpropagation, multi-class discrimination

While lecture notes will serve as the main source of material for the course, the following book constitutes a great reference:
”An Introduction to Statistical Learning (with applications in R)” by James, Witten et al. ISBN: 978-1461471370
”Neural Networks with R” by G. Ciaburro. ISBN: 978-1788397872

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

Learning Objectives: By the end of the course a successful student should:

• Have a solid conceptual grasp on the described statistical learning methods.
• Be able to correctly identify the appropriate techniques to deal with particular data sets.
• Have a working knowledge of R programming software in order to apply those techniques and subse- quently assess the quality of fitted models.
• Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data.

Software: Make sure to download R and RStudio (which can’t be installed without R) before the course starts. Use the link https://www.rstudio.com/products/rstudio/download/ to download it from the mirror appropriate for your platform. Let me know via email in case you encounter difficulties.

Course Outline:
Introduction: What is Statistical Learning?

Supervised and unsupervised learning. Regression and classification.
Linear and Logistic Regression. Continuous response: simple and multiple linear regression. Binary response: logistic regression. Assessing quality of fit.
Model Validation. Validation set approach. Cross-validation.
Tree-based Models. Decision and regression trees: splitting algorithm, tree pruning. Random forests: bootstrap, bagging, random splitting.
Neural Networks. Single-layer perceptron: neuron model, learning weights. Multi-Layer Perceptron: backpropagation, multi-class discrimination

Intro to Statistical Learning. ISBN: 9781461471370

This first course in the sequence Math 4331-4332 provides a solid introduction to deeper properties of the real numbers, continuous functions, differentiability and integration needed for advanced study in mathematics, science and engineering. It is assumed that the student is familiar with the material of Math 3333, including an introduction to the real numbers, basic properties of continuous and differentiable functions on the real line, and an ability to do epsilon-delta proofs.

Topics: Open and closed sets, compact and connected sets, convergence of sequences, Cauchy sequences and completeness, properties of continuous functions, fixed points and the contraction mapping principle, differentiation and integration.

MATH 3331 or equivalent, and three additional hours of 3000-4000 level Mathematics. Previous exposure to Partial Differential Equations (Math 3363) is recommended.

"Partial Differential Equations: An Introduction (second edition)," by Walter A. Strauss, published by Wiley, ISBN-13 978-0470-05456-7  

Description:Initial and boundary value problems, waves and diffusions, reflections, boundary values, Fourier series.

Instructor's Description: will cover the first 6 chapters of the textbook. See the departmental course description.

MATH 3349 or MATH 3349

- Applied Multivariate Statistical Analysis (6th Edition), Pearson. Richard A. Johnson, Dean W. Wichern. ISBN: 978-0131877153 (Required)

- Using R With Multivariate Statistics (1st Edition). Schumacker, R. E. SAGE Publications. ISBN: 978-1483377964 (recommended)

Course Description: Multivariate analysis is a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables. The techniques provide a method for information extraction, regression, or classification. This includes applications of data sets using statistical software.

Course Objectives:

• Understand how to use R and R Markdown
• Understand matrix algebra using R
• Understand the geometry of a sample and random sampling
• Understand the properties of multivariate normal distribution
• Make inferences about a mean vector
• Compare several multivariate means
• Identify and interpret multivariate linear regression models

Course Topics:

• Introduction to R Markdown, Review of R commands (Notes)
• Introduction to Multivariate Analysis (Ch.1)
• Matrix Algebra, R Matrix Commands (Ch.2)
• Sample Geometry and Random Sampling (Ch.3)
• Multivariate Normal Distribution (Ch.4)
• MANOVA (Ch.6)
• Multiple Regression (Ch.7)
• Logistic Regression (Notes)
• Classification (Ch.11)

MATH 3331 or MATH 3321 or equivalent, and three additional hours of 3000-4000 level Mathematics

*Ability to do computer assignments in FORTRAN, C, Matlab, Pascal, Mathematica or Maple.

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 3331 or MATH 3321 or equivalent, and three additional hours of 3000-4000 level Mathematics 

*Ability to do computer assignments in FORTRAN, C, Matlab, Pascal, Mathematica or Maple.

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 2318, or equivalent, and six additional hours of 3000-4000 level Mathematics.

Conditioning and stability of linear systems, matrix factorizations, direct and iterative methods for solving linear systems, computing eigenvalues and eigenvectors, introduction to linear and nonlinear optimization.

Catalog Description: Linear systems of equations, matrices, determinants, vector spaces and linear transformations, eigenvalues and eigenvectors.

Instructor's Description: The course covers the following topics: vector spaces, subspaces, linear combinations,systems of linear equations, linear dependence and linear independence, bases and dimension,linear transformations, null spaces, ranges, matrix rank, matrix inverse and invertibility,determinants and their properties, eigenvalues and eigenvectors, diagonalizability.

MATH 3330 and MATH 3336

Refer to the instructor's syllabus

Description: Divisibility theory, primes and their distribution, theory of congruences and application in security, integer representations, Fermat’s Little Theorem and Euler’s Theorem, primitive roots, quadratic reciprocity, and introduction to cryptography

MATH 3333 or consent of instructor

Graduate standing.

Instructor's notes

Linear Algebra Using MATLAB, Selected material from the text Linear Algebra and Differential Equations Using Matlab by Martin Golubitsky and Michael Dellnitz)
The text will made available to enrolled students free of charge.

Software: Scientific Note Book (SNB) 5.5  (available through MacKichan Software, http://www.mackichan.com/)

Syllabus: Chapter 1 (1.1, 1.3, 1.4), Chapter 2 (2.1-2.5), Chapter 3 (3.1-3.8), Chapter 4 (4.1-4.4), Chapter 5 (5.1-5.2, 5.4-5-6), Chapter 6 (6.1-6.4), Chapter 7 (7.1-7.4), Chapter 8 (8.1)

Project: Applications of linear algebra to demographics. To be completed by the end of the semester as part of the final.

Solving Linear Systems of Equations, Linear Maps and Matrix Algebra, Determinants and Eigenvalues, Vector Spaces, Linear Maps, Orthogonality, Symmetric Matrices, Spectral Theorem.

Students will also learn how to use the computer algebra portion of SNB for completing the project. 

Required Text: Abstract Algebra by David S. Dummit and Richard M. Foote, ISBN: 9780471433347

This book is encyclopedic with good examples and it is one of the few books that includes material for all of the four main topics we will cover: groups, rings, field, and modules.  While some students find it difficult to learn solely from this book, it does provide a nice resource to be used in parallel with class notes or other sources.

Catalog Prerequisite: Graduate standing. Consent of instructor.

Catalog Description: Emphasis on canonical forms and finite dimensional spectral theory.

Catalog Prerequisite: Graduate standing, MATH 2318 and a minimum of 3 semester  hours of 3000-level mathematics

Catalog Description: Linear systems of equations, matrices, determinants, vector spaces and linear transformations, eigenvalues, and eigenvectors. An expository paper or talk on a subject related to the course content is required.

Graduate standing and MATH 3334.

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.

Topics: The course introduces foundational ideas in real analysis, focusing on structure and behavior of functions on subsets of ℝⁿ. Topics include:

• Open and closed sets, compactness, and convergence in ℝⁿ
• Continuity, uniform continuity, and consequences on compact sets
• Differentiation and the Mean Value Theorem
• Riemann integration and the Fundamental Theorem of Calculus

Students will develop proof skills, explore counterexamples, and connect topological ideas with analytic results.

• Required: Lawrence C. Evans, `Partial Differential Equations', Graduate studies in mathematics 19.2 (1998).
• Optional: Robert McOwen, `Partial Differential Equations, Methods and Applications', 2nd Ed. (2004)

Existence and uniqueness theory in partial differential equations; generalized solutions and convergence of approximate solutions to partial differential systems

Catalog prerequisite: Graduate standing. MATH 4331.

Instructor's prerequisite: Graduate standing. MATH 4331 or consent of instructor

(Required) Topology, A First Course, J. R. Munkres, Second Edition, Prentice-Hall Publishers.

link to text

Catalog Description: Point-set topology: compactness, connectedness, quotient spaces, separation properties, Tychonoff’s theorem, the Urysohn lemma, Tietze’s theorem, and the characterization of separable metric spaces

Recommended text: Reading assignments will be a set of selected chapters extracted from the following reference text:

• Introduction to Statistical Learning w/Applications in R, by James , Witten, Hastie, Tibshirani (This book is freely available online). ISBN: 9781461471370
• "Neural Networks with R” by G. Ciaburro. ISBN: 978-1788397872

Summary: A typical task of Machine Learning is to automatically classify observed "cases" or "individuals" into one of several "classes", on the basis of a fixed and possibly large number of features describing each "case". Machine Learning Algorithms (MLAs) implement computationally intensive algorithmic exploration of large set of observed cases. In supervised learning, adequate classification of cases is known for many training cases, and the MLA goal is to generate an accurate Automatic Classification of any new case. In unsupervised learning, no known classification of cases is provided, and the MLA goal is Automatic Clustering, which partitions the set of all cases into disjoint categories (discovered by the MLA).

Numerous MLAs have been developed and applied to images and faces identification, speech understanding, handwriting recognition, texts classification, stock prices anticipation, biomedical data in proteomics and genomics, Web traffic monitoring, etc.

This MSDSfall 2019 course will successively study :

1) Quick Review (Linear Algebra) : multi dimensional vectors, scalar products, matrices,  matrix eigenvectors and eigenvalues, matrix  diagonalization, positive definite matrices

2) Dimension Reduction for  Data Features : Principal Components Analysis (PCA)

3) Automatic Clustering of Data Sets by K-means algorithmics

3) Quick Reviev (Empirical Statistics) : Histograms, Quantiles, Means, Covariance Matrices

4) Computation of Data Features Discriminative Power

5) Automatic Classification by Support Vector Machines (SVMs)  

Emphasis will be on concrete algorithmic implementation and  testing on  actual data sets, as well as on  understanding importants  concepts.

Required Text: ”Neural Networks with R” by G. Ciaburro. ISBN: 9781788397872

• Required: Probability with Applications in Engineering, Science, and Technology, by Matthew A. Carlton and Jay L. Devore, 2014.
• Recommended: Introductory Statistics in R, Peter Dalgaard, 2nd ed., Springer, 2008
• Recommended: Introduction to Probability Models by Sheldon Axler 11th edition
• Lecture Notes

Course Description: Probability, independence, Markov property, Law of Large Numbers, major discrete and continuous distributions, joint distributions and conditional probability, models of convergence, and computational techniques based on the above.

Topics Covered: 

• Probability spaces, random variables, axioms of probability.
• Combinatorial analysis (sampling with, without replacement etc)
• Independence and the Markov property. Markov chains- stochastic processes, Markov property, first step analysis, transition probability matrices. Longterm behavior of Markov chains: communicating classes, transience/recurrence, criteria for transience/recurrence, random walks on the integers.
• Distribution of a random variable, distribution functions, probability density function. Independence.
• Strong law of large numbers and the central limit theorem.
• Major discrete distributions- Bernoulli, Binomial, Poisson, Geometric. Modeling with the major discrete distributions.
• Important continuous distributions- Normal, Exponential. Beta and Gamma.
• Jointly distributed random variables, joint distribution function, joint probability density function, marginal distribution.
• Conditional probability- Bayes theorem. Discrete conditional distributions, continuous conditional distributions, conditional expectations and conditional probabilities. Applications of conditional probability.

• Make sure to download R and RStudio (which can't be installed without R) before the course starts. Use the link RStudio download to download it from the mirror appropriate for your platform.
• **New: Rstudio is in the cloud: RStudio.cloud.

Graduate standing.

This course covers topics in analysis that are motivated by applications.

1. Review of metric spaces, completeness, characterization of compactness, extreme value theorem.
2. Contraction mappings and fixed points. Applications of contractions mappings: integral equations, solutions to initial value problems. Local existence and uniqueness of solutions, stability.
3. Lp spaces as metric completions. Extending the Riemann integral to Lp spaces. Banach spaces.
4. Dual spaces. Uniform boundedness.
5. Consequences of uniform boundedness for Fourier series and polynomial interpolation.
6. Uniform convexity, best approximation property and duality for Lp-spaces. Bounded inverse, closed graph theorem.
7. Hilbert spaces. Orthonormal bases and their characterization. Characterization of best approximation by orthogonal projection. Fourier series.
8. Convergence in L2 and pointwise convergence. Weak convergence.
9. Nonlinear best approximations and (approximate) sparsity.
10. Relationships between weak and norm convergence. Weak compactness in Hilbert spaces. Linear and convex programming in Hilbert spaces.
11. Operators and bilinear forms. The Lax-Milgram theorem.
12. Linear inverse problems. Sparse recovery by norm minimization.
13. The Hilbert-Schmidt norm and Hilbert-Schmidt operators. Compact self-adjoint operators. The spectral theorem for compact, self-adjoint operators.
14. Diagonalizing normal operators. Solutions to Schrodinger's eigenvalue problem and compact integral operators.
    Introduction to the Calculus of Variations.
15. Other topics in coordination with faculty.

Graduate standing and MATH 4331 and MATH 4377

Convex Optimization, Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004

Graduate Standing and must be in the MSDS Program.

Instructor prerequisites: The course is essentially self-contained. The necessary material from statistics and linear algebra is integrated into the course. Background in writing computer programs is preferred but not required.

• "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython", by Wes McKinney, 2 edition, 2017, O'Reilly. (PD) Paper Book. ISBN 13: 9781491957660. Available for free on Safari through UH library.

• "Python for Everybody (Exploring Data in Python3)",  by Dr. Charles Russell Severance, 2016, 1 edition, CreateSpace Independent Publishing Platform (PE) Paper Book. ISBN 13: 9781530051120

Instructor's Description: The course provides essential foundations of Python programming language for developing powerful and reusable data analysis models. The students will get hands-on training on writing programs to facilitate discoveries from data. The topics include data import/export, data types, control statements, functions, basic data processing, and data visualization.

MATH 3334, MATH 3338 and MATH 4378, or consent of instructor.

Graduate standing. MATH 6382 and MATH 6383 

• Required text book for some homework and reference:
  • All of Statistics: A concise course in statistical inference. Larry Wasserman. Springer. 

• Other suggested reading material:
  • Shao. Mathematical Statistics (second edition).
  • Mathematical Statistics: Basic Ideas and Selected Topics, Volume I and II, Bickel and Doksum
  • P. MuCullagh and J.A. Nelder: Generealized Linear Models, 2nd ed. 1999 Chapman Hall/CRC

This is the second of two core statistics courses on mathematical statistics and statistical inference, designed for PhD students in statistics and mathematics. The Probability course (MATH 6382) and the first-semester sequence (MATH 6383) are required prerequisites. This course will cover more advanced topics in statistical inference, statistical computation, and applied statistics. There will be some computational components, and students are expected to use R or Python for statistical computing.

TBA

• Lectures will be based on lecture notes provided by the instructor.
• Suggested reading material:
  • Statistical Methods for Spatial data Analysis by Schabenberger and Gotway, 2005; CRC Press
  • Statistics for spatial data by Noel Cressie, revised edition, Wiley
  • Statistics for spatio-temporal data by Noel Cressie and Christopher K. Wikle, 1st edition, Wiley

This is a graduate level course (multidisciplinary, for Master’s as well as PhD students) that gives a general overview of the field of spatial and spatio-temporal statistics. Students will learn concepts and statistical methods for real data with spatial and temporal dependence. Students will learn to analyze spatial and spatio-temporal data, using R or Python. Various real data application examples will be given during lectures.

Course material and homework assignments will be made available on Canvas. Students will be assessed through practical and theoretical homework assignments and projects.

This course provides students with the mathematical background needed to analyze, implement, and further develop numerical methods at the heart of data-enabled sciences. It is geared towards students who are interested in strengthening their theoretical foundation and honing their skills as a computational scientist and computational mathematician in the emerging field of data science and machine learning. We will review traditional approaches and explore state-of-the-art methods.

This course will be a hands-on experience; while the classes will cover both theory and implementation aspects, the main focus of the assignments will be on implementation aspects. Students will learn how to write mathematical code to solve “simple” data science problems. The focus is not to apply existing methods but rather to understand the foundational concepts by implementing mathematically sound methods from scratch. This will enable students to better understand when modern machine learning methods will work, and when they will fail. Students are free to use their preferred programming language. This course will also touch up on topics in numerical analysis, numerical linear algebra, and optimization applied to machine learning and data science.

View the instructor's syllabus

TBA

Walter Rudin, Functional Analysis, 2nd edition. McGraw Hill, 1991. (Instructor may suggest other tests or have their own typed notes)

Catalog description: Linear topological spaces, Banach and Hilbert spaces, duality, and spectral analysis.

Instructor's description: TBD

View the instructor's syllabus

Manifolds and tangent bundles, submanifolds and imbeddings, integral manifolds, triangulation of manifolds, connections and holonomy; Riemannian geometry, surface theory, Morse theory, and G-structures.

View the instructor's syllabus

Manifolds and tangent bundles, submanifolds and imbeddings, integral manifolds, triangulation of manifolds, connections and holonomy; Riemannian geometry, surface theory, Morse theory, and G-structures.