Twin Cities campus
 
Twin Cities Campus

Data Science M.S.

Computer Science and Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Data Science Graduate Program, Department of Computer Science and Engineering, University of Minnesota, 4-192 Keller Hall, 200 Union Street S.E., Minneapolis, MN 55455 (612- 625-4002; fax: 612-625-0572).
  • Program Type: Master's
  • Requirements for this program are current for Spring 2022
  • Length of program in credits: 31
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The Data Science MS program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods. Students who graduate from this regular 2-year master's program will learn the state-of-the-art methods for treating Big Data, be exposed to the cutting-edge methods and theory forming the basis for the next generation of Big Data technology, and will complete a project demonstrating that they can use the fundamental concepts to design innovative methods for new application areas arising from business, government, security, medicine, biology, physical sciences, and the environment.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
  • partially online (between 50% to 80% of instruction is online)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
A bachelor's degree from an accredited college or university in computer science, math, statistics, engineering, natural sciences, or a related field.
Other requirements to be completed before admission:
The undergraduate degree must include statistics, calculus, multivariable calculus, linear algebra, and mathematical software environments such as Matlab or R or the equivalent, programming languages such as C+, C++, Java, programming experience including algorithms and data structures normally taught in beginning computer science courses either as part of the undergraduate degree or subsequent work experience.
Special Application Requirements:
The application deadline is March 1. Applicants are only considered for fall admission and decisions are made after all applications are received following the close of the application cycle. GRE test scores are not required, but are recommended for those applying from international institutions. If submitted, the GRE is only one of many factors considered for admission, and no score will guarantee or preclude admission. Applications without the GRE will be considered based on the material submitted.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Writing Score: 23
    • Internet Based - Reading Score: 23
  • IELTS
    • Total Score: 6.5
  • MELAB
    • Part 1 (Composition) score: 80
Key to test abbreviations (TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan B: Plan B requires 31 major credits and 0 credits outside the major. The final exam is written and oral. A capstone project is required.
Capstone Project:Students must complete 3 credit hours of DSCI 8760 (capstone project) under the supervision of a faculty member.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.25 is required for students to remain in good standing.
Courses offered on both the A-F and S/N grading basis must be taken A-F. At least 3 8xxx-level credits, either from an emphasis or an elective, are required. Complete a presentation at the Data Science Poster Fair for Plan B project as part of degree requirements in the semester of anticipated graduation. Consult with the advisor on an appropriate timeline to present.
Statistics (6 credits)
Statistics Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
STAT 5102 - Theory of Statistics II (4.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
MATH 5651 - Basic Theory of Probability and Statistics (4.0 cr)
or STAT 5101 - Theory of Statistics I (4.0 cr)
Statistics Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to meet the 6-credit Statistics requirement:
EE 5531 - Probability and Stochastic Processes (3.0 cr)
EE 8581 - Detection and Estimation Theory (3.0 cr)
PUBH 7405 - Biostatistical Inference I (4.0 cr)
PUBH 7406 - Biostatistical Inference II (3.0 cr)
PUBH 7407 - Analysis of Categorical Data (3.0 cr)
PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
PUBH 7460 - Advanced Statistical Computing (3.0 cr)
PUBH 7485 - Methods for Causal Inference (3.0 cr)
PUBH 8401 - Linear Models (4.0 cr)
PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
STAT 5052 - Statistical and Machine Learning (3.0 cr)
STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)
STAT 5303 - Designing Experiments (4.0 cr)
STAT 5421 - Analysis of Categorical Data (3.0 cr)
STAT 5601 - Nonparametric Methods (3.0 cr)
STAT 5701 - Statistical Computing (3.0 cr)
STAT 8112 - Mathematical Statistics II (3.0 cr)
AST 5731 - Bayesian Astrostatistics (4.0 cr)
or STAT 5731 - Bayesian Astrostatistics (4.0 cr)
Algorithmics (6 credits)
Algorithmics Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
EE 8591 - Predictive Learning from Data (3.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
PUBH 8475 - Statistical Learning and Data Mining (3.0 cr)
Algorithmics Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to meet the 6-credit Algorithmics requirement:
CSCI 5302 - Analysis of Numerical Algorithms (3.0 cr)
CSCI 5304 - Computational Aspects of Matrix Theory (3.0 cr)
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5512 - Artificial Intelligence II (3.0 cr)
CSCI 5609 - Visualization (3.0 cr)
CSCI 8314 - Sparse Matrix Computations (3.0 cr)
CSCI 8581 - Big Data in Astrophysics (4.0 cr)
EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
EE 5251 - Optimal Filtering and Estimation (3.0 cr)
EE 5389 - Introduction to Predictive Learning (3.0 cr)
EE 5391 - Computing With Neural Networks (3.0 cr)
EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
EE 5551 - Multiscale and Multirate Signal Processing (3.0 cr)
EE 5561 - Image Processing and Applications (3.0 cr)
EE 5581 - Information Theory and Coding (3.0 cr)
EE 5585 - Data Compression (3.0 cr)
EE 8231 - Optimization Theory (3.0 cr)
IE 5531 - Engineering Optimization I (4.0 cr)
IE 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
Infrastructure and Large-Scale Computing (6 credits)
Infrastructure and Large-Scale Computing Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
CSCI 5707 - Principles of Database Systems (3.0 cr)
CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
EE 5351 - Applied Parallel Programming (3.0 cr)
CSCI 8205 - Parallel Computer Organization (3.0 cr)
or EE 8367 - Parallel Computer Organization (3.0 cr)
Infrastructure and Large-Scale Computing Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to complete the 6-credit Infrastructure and Large-Scale Computing requirement.
CSCI 5103 - Operating Systems (3.0 cr)
CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
CSCI 5231 {Inactive} (3.0 cr)
CSCI 5271 - Introduction to Computer Security (3.0 cr)
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science (3.0 cr)
CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)
CSCI 5801 - Software Engineering I (3.0 cr)
CSCI 5802 - Software Engineering II (3.0 cr)
CSCI 8102 - Foundations of Distributed Computing (3.0 cr)
CSCI 8701 - Overview of Database Research (3.0 cr)
CSCI 8715 - Spatial Data Science Research (3.0 cr)
CSCI 8725 - Databases for Bioinformatics (3.0 cr)
CSCI 8735 - Advanced Database Systems (3.0 cr)
CSCI 8801 - Advanced Software Engineering (3.0 cr)
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing (3.0 cr)
EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
EE 5381 - Telecommunications Networks (3.0 cr)
EE 5501 - Digital Communication (3.0 cr)
Electives (9 credits)
Select 9 credits from the following in consultation with the advisor. Courses from above lists that are not applied to other requirements can be selected with advisor approval. Other electives may be selected in consultation with the advisor and director of graduate studies. If 3 credits of DSCI 8760 have already been taken in a semester an additional 3 credits in a subsequent semester can be used towards elective coursework after consultation with the advisor.
CSCI 5106 - Programming Languages (3.0 cr)
CSCI 5123 - Recommender Systems (3.0 cr)
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics (3.0 cr)
CSCI 5561 - Computer Vision (3.0 cr)
CSCI 5980 - Special Topics in Computer Science (1.0-3.0 cr)
CSCI 8271 - Security and Privacy in Computing (3.0 cr)
CSCI 8363 - Numerical Linear Algebra in Data Exploration (3.0 cr)
CSCI 8980 - Special Advanced Topics in Computer Science (1.0-3.0 cr)
DSCI 8760 - Data Science M.S. Plan B Project (3.0 cr)
EE 5393 - Circuits, Computation, and Biology (3.0 cr)
IE 8534 - Advanced Topics in Operations Research (1.0-4.0 cr)
IE 8535 - Introduction to Network Science (4.0 cr)
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 8446 - Advanced Statistical Genetics and Genomics (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
Research Colloquium (1 credit)
Select 1 of the following in consultation with the advisor:
CSCI 8970 - Computer Science Colloquium (1.0 cr)
or DSCI 8970 - Data Science M.S. Colloquium (1.0 cr)
Capstone Course (3 credits)
Take the following in consultation with the advisor:
DSCI 8760 - Data Science M.S. Plan B Project (3.0 cr)
 
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PUBH 7401 - Fundamentals of Biostatistical Inference
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth
PUBH 7402 - Biostatistics Modeling and Methods
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences
PUBH 7440 - Introduction to Bayesian Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent
STAT 5102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent
STAT 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent
MATH 5651 - Basic Theory of Probability and Statistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 5651/Stat 5101
Typically offered: Every Fall & Spring
Logical development of probability, basic issues in statistics. Probability spaces, random variables, their distributions/expected values. Law of large numbers, central limit theorem, generating functions, sampling, sufficiency, estimation. prereq: [2263 or 2374 or 2573], [2243 or 2373]; [2283 or 2574 or 3283] recommended.
STAT 5101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (CSCI 2033 or MATH 2373 or MATH 2243)
EE 5531 - Probability and Stochastic Processes
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks. prereq: [3025, CSE grad student] or dept consent
EE 8581 - Detection and Estimation Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Risk theory approach to detection and estimation, random process representation, signal parameter estimation. Waveform estimation; detection of phase, frequency, and delay in signals. Applications to communications and radar-sonar signal design and processing. prereq: 5531 or instr consent
PUBH 7405 - Biostatistical Inference I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent
PUBH 7406 - Biostatistical Inference II
Credits: 3.0 [max 4.0]
Typically offered: Every Spring
Topics include maximum likelihood estimation, single and multifactor analysis of variance, logistic regression, log-linear models, multinomial logit models, proportional odds models for ordinal data, gamma and inverse-Gaussian models, over-dispersion, analysis of deviance, model selection and criticism, model diagnostics, and an introduction to non-parametric regression methods. R is used. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics major] or instr consent
PUBH 7407 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Contingency tables, odds ratio, relative risk, chi-square tests, log-linear models, logistic regression, conditional logistic regression, Poisson regression, matching, generalized linear models for independent data. SAS/S-Plus used throughout. prereq: 7405, [Stat 5102 or concurrent registration is required (or allowed) in Stat 5102 or Stat 8102 or concurrent registration is required (or allowed) in Stat 8102]
PUBH 7430 - Statistical Methods for Correlated Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).
PUBH 7460 - Advanced Statistical Computing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent
PUBH 7485 - Methods for Causal Inference
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. The first part of this course focuses on methods to establish the causal effect of a point exposure, i.e., situations in which treatment is given at a single point in time. Methods to estimate causal treatment effects will include outcome regression, propensity score methods (i.e., inverse weighting, matching), and doubly robust approaches. The second half of the course focuses on estimating the effect of a series of treatment decisions during the course of a chronic disease such as cancer, substance abuse, mental health disorders, etc. Methods to estimate these time-varying treatments include marginal structural models estimated by inverse probability weighting, structural nested models estimated by G-estimation, and the (parametric) G-computation algorithm. We will then turn our attention to estimating the optimal treatment sequence for a given subject, i.e., how to determine “the right treatment, for the right patient, at the right time,” using dynamic marginal structural models and methods derived from reinforcement learning (e.g., Q-learning, A-learning) and classification problems (outcome weighted learning, C-learning). PubH 8485 is appropriate for Ph.D students in Biostatistics and Statistics. The homework and projects will focus more on the theoretical aspects of the methods to prepare students for methodological research in this area. PubH 7485 is appropriate for Masters students in Biostatistics and PhD students in other fields who wish to learn causal methods to apply them to topics in the health sciences. This course uses the statistical software of R, a freely available statistical software package, to implement many of the methods we discuss. However, most of the methods discussed in this course can be implemented in any statistical software (e.g., SAS, Stata, SPSS, etc.) and students will be free to use any software for homework assignments. prereq: Background in regression (e.g. linear, logistic, models) at the level of PubH 7405-7406, PubH 6450-6451, PubH 7402, or equiv. Background in statistical theory (Stat 5101-5102 or PubH 7401) is helpful.
PUBH 8401 - Linear Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory/application of statistical techniques for regression analysis. Computing for linear models. Modeling, computation, data analysis. prereq: [[7405, concurrent registration is required (or allowed) in STAT 8101] or instr consent], calculus, familiar wtih matrix/linear algebra
PUBH 8432 - Probability Models for Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
STAT 5052 - Statistical and Machine Learning
Credits: 3.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
The material covered will be the foundations of modern machine learning methods including regularization methods, discriminant analysis, neural nets, random forest, bagging, boosting, support vector machine, and clustering. Model comparison using cross-validation and bootstrap methods will be emphasized.
STAT 5201 - Sampling Methodology in Finite Populations
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Simple random, systematic, stratified, unequal probability sampling. Ratio, model based estimation. Single stage, multistage, adaptive cluster sampling. Spatial sampling. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5303 - Designing Experiments
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent
STAT 5701 - Statistical Computing
Credits: 3.0 [max 3.0]
Prerequisites: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Statistical programming, function writing, graphics using high-level statistical computing languages. Data management, parallel computing, version control, simulation studies, power calculations. Using optimization to fit statistical models. Monte Carlo methods, reproducible research. prereq: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
STAT 8112 - Mathematical Statistics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference, estimation, and hypothesis testing. Convergence and relationship between convergence modes. Asymptotics of maximum likelihood estimators, distribution functions, quantiles. Delta method. prereq: 8111
AST 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
STAT 5731 - Bayesian Astrostatistics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 5731/Stat 5731
Grading Basis: A-F only
Typically offered: Every Fall
This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets and a focus on modern statistical software, such as R and python. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent Suggested: statistical course at the level of AST 4031, AST 5031, STAT 3021, or STAT 5021
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
EE 8591 - Predictive Learning from Data
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of ‘new’ algorithms. prereq: CSE grad student or instr consent
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
PUBH 8475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7475/PubH 8475/Stat 8056
Typically offered: Periodic Spring
Statistical techniques for extracting useful information from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging/boosting), unsupervised learning. prereq: [[[6450, 6451, 6452] or STAT 5303 or equiv], [biostatistics or statistics PhD student]] or instr consent
CSCI 5302 - Analysis of Numerical Algorithms
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Additional topics in numerical analysis. Interpolation, approximation, extrapolation, numerical integration/differentiation, numerical solutions of ordinary differential equations. Introduction to optimization techniques. prereq: 2031 or 2033 or instr consent
CSCI 5304 - Computational Aspects of Matrix Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Perturbation theory for linear systems and eigenvalue problems. Direct/iterative solution of large linear systems. Matrix factorizations. Computation of eigenvalues/eigenvectors. Singular value decomposition. LAPACK/other software packages. Introduction to sparse matrix methods. prereq: 2031 or 2033 or instr consent
CSCI 5511 - Artificial Intelligence I
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4511W/CSci 5511
Prerequisites: [2041 or #], grad student
Typically offered: Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student
CSCI 5512 - Artificial Intelligence II
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 5512W/CSci 5512
Typically offered: Every Spring
Uncertainty in artificial intelligence. Probability as a model of uncertainty, methods for reasoning/learning under uncertainty, utility theory, decision-theoretic methods. prereq: [STAT 3021, 4041] or instr consent
CSCI 5609 - Visualization
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Fundamental theory/practice in data visualization. Programming applications. Perceptual issues in effective data representation, multivariate visualization, information visualization, vector field/volume visualization. prereq: [1913, 4041] or equiv or instr consent
CSCI 8314 - Sparse Matrix Computations
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Sparsity and sparse matrices. Data structures for sparse matrices. Direct methods for sparse linear systems. Reordering techniques to reduce fill-in such as minimal degree ordering and nested dissection ordering. Iterative methods. Preconditioning algorithms. Algorithms for sparse eigenvalue problems and sparse least-squares. prereq: 5304 or numerical linear algebra course or instr consent
CSCI 8581 - Big Data in Astrophysics
Credits: 4.0 [max 4.0]
Course Equivalencies: Ast 8581/CSci 8581/Phys 8581
Grading Basis: A-F only
Typically offered: Every Spring
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. Prerequisites: MATH 2263 and MATH 2243, or equivalent; or instructor consent. Suggested: familiarity with astrophysics topics such as star formation and evolution, galaxies and clusters, composition and expansion of the universe, gravitational wave sources and waveforms, and high-energy astrophysics.
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5251 - Optimal Filtering and Estimation
Credits: 3.0 [max 3.0]
Course Equivalencies: AEM 5451/EE 5251
Typically offered: Every Fall
Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications. prereq: [[[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; 3025, 4231 recommended
EE 5389 - Introduction to Predictive Learning
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 4389W/EE 5389
Typically offered: Fall Even Year
Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies. prereq: EE 3025, STAT 3022 or equivalent; computer programming or MATLAB or similar environment is recommended.
EE 5391 - Computing With Neural Networks
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Neural networks as a computational model. Connections to AI, statistics and model-based computation. Associative memory and matrix computation; Hopfield networks. Supervised networks for classification and prediction. Unsupervised networks for data reduction. Associative recognition/retrieval, optimization, time series prediction, knowledge extraction. prereq: [[3025 or Stat 3091], CSE grad student] or dept consent
EE 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, CSE grad student] or dept consent
EE 5551 - Multiscale and Multirate Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Multirate discrete-time systems. Bases, frames. Continuous wavelet transform. Scaling equations. Discrete wavelet transform. Applications in signal/image processing. prereq: [4541, 5531, CSE grad student] or dept consent
EE 5561 - Image Processing and Applications
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5561/EE 8541
Typically offered: Every Spring
Two-dimensional digital filtering/transforms. Application to image enhancement, restoration, compression, and segmentation. prereq: [4541, 5581, CSE grad student] or instr consent
EE 5581 - Information Theory and Coding
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Source/channel models, codes for sources/channels. Entropy, mutual information, capacity, rate-distortion functions. Coding theorems. prereq: [5531, CSE grad student] or dept consent
EE 5585 - Data Compression
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Source coding in digital communications and recording. Codes for lossless compression. Universal lossless codes. Lossless image compression. Scalar and vector quantizer design. Loss source coding theory. Differential coding, trellis codes, transform/subband coding. Analysis/synthesis schemes. prereq: CSE grad student or dept consent
EE 8231 - Optimization Theory
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Introduction to optimization in engineering; approximation theory. Least squares estimation, optimal control theory, and computational approaches. prereq: instr consent
IE 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 8521 - Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.
IE 8531 - Discrete Optimization
Credits: 4.0 [max 8.0]
Typically offered: Every Fall & Spring
Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.
CSCI 5105 - Introduction to Distributed Systems
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Distributed system design and implementation. Distributed communication and synchronization, data replication and consistency, distributed file systems, fault tolerance, and distributed scheduling. prereq: [5103 or equiv] or instr consent
CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads. prereq: 4041 or instr consent
CSCI 5707 - Principles of Database Systems
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4707/CSci 5707/INET 4707
Typically offered: Every Fall
Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends. prereq: [4041 or instr consent], grad student
CSCI 5708 - Architecture and Implementation of Database Management Systems
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Techniques in commercial/research-oriented database systems. Catalogs. Physical storage techniques. Query processing/optimization. Transaction management. Mechanisms for concurrency control, disaster recovery, distribution, security, integrity, extended data types, triggers, and rules. prereq: 4707 or 5707 or instr consent
EE 5351 - Applied Parallel Programming
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies. prereq: [4363 or equivalent], programming experience (C/C++ preferred)
CSCI 8205 - Parallel Computer Organization
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 8205/EE 8367
Typically offered: Every Spring
Design/implementation of multiprocessor systems. Parallel machine organization, system design. Differences between parallel, uniprocessor machines. Programming models. Synchronization/communication. Topologies, message routing strategies. Performance optimization techniques. Compiler, system software issues. prereq: 5204 or EE 5364 or instr consent
EE 8367 - Parallel Computer Organization
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 8205/EE 8367
Typically offered: Every Spring
Design/implementation of multiprocessor systems. Parallel machine organization, system design. Differences between parallel, uniprocessor machines. Programming models. Synchronization/communication. Topologies, message routing strategies. Performance optimization techniques. Compiler, system software issues. prereq: 5364 or CSci 5204
CSCI 5103 - Operating Systems
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Conceptual foundation of operating system designs and implementations. Relationships between operating system structures and machine architectures. UNIX implementation mechanisms as examples. prereq: 4061 or instr consent
CSCI 5211 - Data Communications and Computer Networks
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4211/CSci 5211/INET 4002
Typically offered: Every Fall
Concepts, principles, protocols, and applications of computer networks. Layered network architectures, data link protocols, local area networks, network layer/routing protocols, transport, congestion/flow control, emerging high-speed networks, network programming interfaces, networked applications. Case studies using Ethernet, Token Ring, FDDI, TCP/IP, ATM, Email, HTTP, and WWW. prereq: [4061 or instr consent], basic knowledge of [computer architecture, operating systems, probability], grad student
CSCI 5271 - Introduction to Computer Security
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Concepts of computer, network, and information security. Risk analysis, authentication, access control, security evaluation, audit trails, cryptography, network/database/application security, viruses, firewalls. prereq: 4061 or equiv or instr consent
CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Spatial databases and querying, spatial big data mining, spatial data-structures and algorithms, positioning, earth observation, cartography, and geo-visulization. Trends such as spatio-temporal, and geospatial cloud analytics, etc. prereq: Familiarity with Java, C++, or Python
CSCI 5751 - Big Data Engineering and Architecture
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Big data and data-intensive application management, design and processing concepts. Data modeling on different NoSQL databases: key/value, column-family, document, graph-based stores. Stream and real-time processing. Big data architectures. Distributed computing using Spark, Hadoop or other distributed systems. Big data projects. prereq: 4041, 5707, or instructor consent.
CSCI 5801 - Software Engineering I
Credits: 3.0 [max 3.0]
Prerequisites: 2041 or #
Typically offered: Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent
CSCI 5802 - Software Engineering II
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Introduction to software testing, software maturity models, cost specification models, bug estimation, software reliability models, software complexity, quality control, and experience report. Student groups specify, design, implement, and test partial software systems. Application of general software development methods and principles from 5801. prereq: 5801 or instr consent
CSCI 8102 - Foundations of Distributed Computing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Fundamental principles underlying design of distributed and multiprocessor operating systems. Foundations of distributed computing systems; shared multiprocessor systems. prereq: 8101 or instr consent
CSCI 8701 - Overview of Database Research
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Research papers from journals and conferences on current topics in databases, such as database research methodologies, relational implementation techniques, active databases, storage systems, benchmarking, distributed and parallel databases, new data models, prototype systems, data mining, and future directions. prereq: 5708 or instr consent
CSCI 8715 - Spatial Data Science Research
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Motivation, models of spatial information, querying spatial data, processing strategies for spatial queries, multi-dimensional storage/access methods, spatial graph datasets, spatial data mining, trends (e.g., spatio-temporal databases, mobile objects, raster databases), research literature, how to pursue research. prereq: 4707 or 5707 or 5715 or GIS 5571 or GIS 5573
CSCI 8725 - Databases for Bioinformatics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
DBMS support for biological databases, data models. Searching integrated public domain databases. Queries/analyses, DBMS extensions, emerging applications. prereq: 4707 or 5707 or instr consent
CSCI 8735 - Advanced Database Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Database systems for emerging applications, nontraditional query processors, multi-dimensional data indexing. Current research trends. prereq: 4707 or 5707 or 5708
CSCI 8801 - Advanced Software Engineering
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Software reusability, internet/intranet programming, software reengineering, and software safety. prereq: 5801 or instr consent
EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
Credits: 3.0 [max 3.0]
Typically offered: Spring Odd Year
Algorithm techniques for enhancing the scalability of parallel software: scatter-to-gather, problem decomposition, binning, privatization, tiling, regularization, compaction, double-buffering, and data layout. These techniques address the most challenging problems in building scalable parallel software: limited parallelism, data contention, insufficient memory bandwidth, load balance, and communication latency. Programming assignments will be given to reinforce the understanding of the techniques. prereq: basic knowledge of CUDA, experience working in a Unix environment, and experience developing and running scientific codes written in C or C++. Completion of EE 5351 is not required but highly recommended.
EE 5371 - Computer Systems Performance Measurement and Evaluation
Credits: 3.0 [max 3.0]
Course Equivalencies: EE 5371/5863
Typically offered: Periodic Fall & Spring
Tools/techniques for analyzing computer hardware, software, system performance. Benchmark programs, measurement tools, performance metrics. Deterministic/probabilistic simulation techniques, random number generation/testing. Bottleneck analysis. prereq: [4363 or 5361 or CSci 4203 or 5201], [CSE grad student] or dept consent
EE 5381 - Telecommunications Networks
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Fundamental concepts of modern telecommunications networks, mathematical tools required for their performance analysis. Layered network architecture, point-to-point protocols/links, delay models, multiaccess communication/routing. prereq: [4501, 5531, CSE grad student] or dept consent
EE 5501 - Digital Communication
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theory/techniques of modern digital communications. Communication limits. Modulation/detection. Data transmission over channels with intersymbol interference. Optimal/suboptimal sequence detection. Equalization. Error correction coding. Trellis-coded modulation. Multiple access. prereq: [3025, 4501, CSE grad student] or dept consent
CSCI 5106 - Programming Languages
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages. prereq: 4011 or instr consent
CSCI 5123 - Recommender Systems
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
An overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. prereq: Java programming and 2033 and 3081, or instructor consent.
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Computational methods for analyzing, integrating, and deriving predictions from genomic/proteomic data. Analyzing gene expression, proteomic data, and protein-protein interaction networks. Protein/gene function prediction, Integrating diverse data, visualizing genomic datasets. prereq: 3003 or 4041 or instr consent
CSCI 5561 - Computer Vision
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Issues in perspective transformations, edge detection, image filtering, image segmentation, and feature tracking. Complex problems in shape recovery, stereo, active vision, autonomous navigation, shadows, and physics-based vision. Applications. prereq: CSci 5511, 5521, or instructor consent.
CSCI 5980 - Special Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Periodic Fall & Spring
Lectures and informal discussions on current topics in computer science. prereq: instr consent; may be repeated for cr
CSCI 8271 - Security and Privacy in Computing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall
Recent security/privacy issues in computer systems/networks. Threats, attacks, countermeasures. Security research, authentication, network security, wireless security, computer system security, anonymous system, pseudonym, access control, intrusion detection system, cryptographic protocols. How to pursue research in security and design secure systems. prereq: [5211, 5103] or instr consent; 5471 or EE 5248 or Math 5248 or equiv recommended
CSCI 8363 - Numerical Linear Algebra in Data Exploration
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Computational methods in linear algebra, matrix decompositions for linear equations, least squares, eigenvalue problems, singular value decomposition, conditioning, stability in method for machine learning, large data collections. Principal directions, unsupervised clustering, latent semantic indexing, linear least squares fit. Markov chain models on hyperlink structure. prereq: 5304 or instr consent
CSCI 8980 - Special Advanced Topics in Computer Science
Credits: 1.0 -3.0 [max 27.0]
Typically offered: Every Fall & Spring
Lectures and informal discussions. prereq: instr consent
DSCI 8760 - Data Science M.S. Plan B Project
Credits: 3.0 [max 6.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
Project arranged between student and faculty.
EE 5393 - Circuits, Computation, and Biology
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Connections between digital circuit design and synthetic/computational biology. Probabilistic, discrete-event simulation. Timing analysis. Information-Theoretic Analysis. Feedback in digital circuits/genetic regulatory systems. Synthesizing stochastic logic and probabilistic biochemistry.
IE 8534 - Advanced Topics in Operations Research
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Every Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks.
IE 8535 - Introduction to Network Science
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Topics include deterministic and random networks, network flows, matching, game theory, distributed decision making in networks, cooperation in networks, cascades in networks, wisdom of crowds, applications in voting, prediction markets, consumer behavior modeling, revenue management, inventory control and finance. This course is offered to graduate students. Undergraduate students must get permission from the instructor for registering. Prerequisites include probability and optimization (5531 and 8532) but students who have taken similar courses or have the mathematical background can register by instructor permission.
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended
PUBH 7445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 8445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7445/PubH 8445
Typically offered: Fall Odd Year
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable
PUBH 8446 - Advanced Statistical Genetics and Genomics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent
PUBH 8472 - Spatial Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended
CSCI 8970 - Computer Science Colloquium
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures.
DSCI 8970 - Data Science M.S. Colloquium
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall
Recent developments in Data Science and related disciplines. Students must attend 13 of the 15 lectures.
DSCI 8760 - Data Science M.S. Plan B Project
Credits: 3.0 [max 6.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
Project arranged between student and faculty.