Timeroom: Spring 2025

Displaying 121 - 130 of 144 Results for: Subject = MATH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 838 (01) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 52728
An introduction to supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Logistic Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models. Required case studies and projects provide students with experience in applying these techniques and strategies. The course necessarily involves the use of statistical software and programming languages. Students must have completed a calculus-based introductory statistics course.
Cross listed with : MATH 738.01
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MW 12:40pm - 2:00pm KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 838 (02) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 51833
An introduction to supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Logistic Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models. Required case studies and projects provide students with experience in applying these techniques and strategies. The course necessarily involves the use of statistical software and programming languages. Students must have completed a calculus-based introductory statistics course.
Cross listed with : MATH 738.02
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 12:40pm - 2:00pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 840 (01) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 51279
First course in design of experiments with applications to quality improvement in industrial manufacturing, engineering research and development, or research in physical and biological sciences. Experimental factor identification, statistical analysis and modeling of experimental results, randomization and blocking, full factorial designs, random and mixed effects models, replication and sub-sampling strategies, fractional factorial designs, response surface methods, mixture designs, and screening designs. Focuses on various treatment structures for designed experimentation and the associated statistical analyses. Use of statistical software. Students must have completed an introductory statistics course.
Cross listed with : MATH 740.01
Instructors: Pei Geng
Start Date End Date Days Time Location
1/21/2025 5/5/2025 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 843 (01) - Time Series Analysis

Time Series Analysis

Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 56502
An introduction to univariate time series models and associated methods of data analysis and inference in the time domain and frequency domain. Topics include: Auto regressive (AR), moving average (MA), ARMA and ARIMA processes, stationary and non-stationary processes, seasonal ARIMA processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral density function, periodogram and discrete Fourier transform, linear filters. parametric spectral estimation, dynamic Fourier analysis. Additional topics may include wavelets and long memory processes (FARIMA) and GARCH Models. The use of statistical software, such as JMP, or R, is fully integrated in to the course. Offered in alternate years in the spring.
Prerequisite(s): (MATH 835 with minimum grade of B- or MATH 839 with minimum grade of B- )
Cross listed with : MATH 743.01
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 9:40am - 11:00am HS 108
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 843 (02) - Time Series Analysis

Time Series Analysis

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 56504
An introduction to univariate time series models and associated methods of data analysis and inference in the time domain and frequency domain. Topics include: Auto regressive (AR), moving average (MA), ARMA and ARIMA processes, stationary and non-stationary processes, seasonal ARIMA processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral density function, periodogram and discrete Fourier transform, linear filters. parametric spectral estimation, dynamic Fourier analysis. Additional topics may include wavelets and long memory processes (FARIMA) and GARCH Models. The use of statistical software, such as JMP, or R, is fully integrated in to the course. Offered in alternate years in the spring.
Prerequisite(s): (MATH 835 with minimum grade of B- or MATH 839 with minimum grade of B- )
Cross listed with : MATH 743.02
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 9:40am - 11:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 844 (01) - Design of Experiments II

Design of Experiments II

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 56667
Second course in design of experiments, with applications in quality improvement and industrial manufacturing, engineering research and development, research in physical and biological sciences. Covers experimental design strategies and issues that are often encountered in practice complete and incomplete blocking, partially balanced incomplete blocking (PBIB), partial confounding, intra and inter block information, split plotting and strip plotting, repeated measures, crossover designs, Latin squares and rectangles, Youden squares, crossed and nested treatment structures, variance components, mixed effects models, analysis of covariance, optimizations, space filling designs, and modern screening design strategies.
Prerequisite(s): MATH 840 with minimum grade of B-
Cross listed with : MATH 744.01
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/21/2025 5/5/2025 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 847 (01) - Introduction to Nonlinear Dynamics and Chaos

Intro Nonlinear Dynamics&Chaos

Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 55016
An introduction to the mathematics of chaos and nonlinear dynamics. Topics include: linear and nonlinear systems of ordinary differential equations; discrete maps; chaos; phase plane analysis; bifurcations; and computer simulations. Students taking this course are required to have some background in elementary differential equations, linear algebra, and multidimensional calculus. (Not offered every year.)
Cross listed with : MATH 747.01
Instructors: Kevin Short
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MW 2:10pm - 4:00pm MURK G04
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 856 (01) - Principles of Statistical Inference

Princpls Statistical Inference

Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 52729
Introduces the basic principles and methods of statistical estimation and model fitting. One- and two-sample procedures, consistency and efficiency, likelihood methods, confidence regions, significance testing, Bayesian inference, nonparametric and re-sampling methods, decision theory.
Prerequisite(s): MATH 855 with minimum grade of B-
Cross listed with : MATH 756.01
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 11:10am - 12:30pm KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 856 (02) - Principles of Statistical Inference

Princpls Statistical Inference

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 50451
Introduces the basic principles and methods of statistical estimation and model fitting. One- and two-sample procedures, consistency and efficiency, likelihood methods, confidence regions, significance testing, Bayesian inference, nonparametric and re-sampling methods, decision theory.
Prerequisite(s): MATH 855 with minimum grade of B-
Cross listed with : MATH 756.02
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 11:10am - 12:30pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 857 (01) - Mathematical Optimization for Applications

Mathematical Optimization

Credits: 3.0
Term: Spring 2025 - Full Term (01/21/2025 - 05/05/2025)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 52858
This course introduces the foundations of mathematical optimization and reinforces them via applications. The content includes convex optimization, first and second-order methods, constrained problems, duality, linear and quadratic programming, as well as discrete and non-convex optimization. Applications will focus on machine learning methods but also include problems from engineering and operations research. Students are required to have a mastery of Calculus II and programming proficiency in MATLAB, R, Java, C, Python, or equivalent.
Equivalent(s): CS 857
Cross listed with : MATH 757.01
Instructors: Marek Petrik
Start Date End Date Days Time Location
1/21/2025 5/5/2025 MWF 10:10am - 11:00am KING N113