Timeroom: Spring 2024

Displaying 111 - 120 of 132 Results for: Subject = MATH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 836 (01) - Advanced Statistical Modeling

Advanced Statistical Modeling

Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 53691
This is a course on statistical models behind normal linear model. Topics covered in this course include generalized linear model, linear mixed model, generalized additive model, generalized linear mixed model, generalized additive mixed model, and smoothing methods if time allows.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): (MATH 835 with minimum grade of B- or MATH 839 with minimum grade of B- )
Cross listed with : MATH 736.01
Instructors: Qi Zhang
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 8:10am - 9:30am KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 836 (02) - Advanced Statistical Modeling

Advanced Statistical Modeling

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 53779
This is a course on statistical models behind normal linear model. Topics covered in this course include generalized linear model, linear mixed model, generalized additive model, generalized linear mixed model, generalized additive mixed model, and smoothing methods if time allows.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): (MATH 835 with minimum grade of B- or MATH 839 with minimum grade of B- )
Instructors: STAFF
Start Date End Date Days Time Location
1/23/2024 5/6/2024 Hours Arranged TBA
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 838 (01) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 53389
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross listed with : MATH 738.01
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/23/2024 5/6/2024 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: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 52160
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Instructors: STAFF
Start Date End Date Days Time Location
1/23/2024 5/6/2024 Hours Arranged TBA
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 840 (01) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 51452
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross listed with : MATH 740.01
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/23/2024 5/6/2024 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 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 57024
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.)
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross listed with : MATH 747.01
Instructors: Kevin Short
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MW 2:10pm - 4:00pm HS 150
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 856 (01) - Principles of Statistical Inference

Princpls Statistical Inference

Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 53390
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 855 with minimum grade of B-
Cross listed with : MATH 756.01
Instructors: Pei Geng
Start Date End Date Days Time Location
1/23/2024 5/6/2024 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: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 50527
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 855 with minimum grade of B-
Cross listed with : MATH 756.02
Instructors: Pei Geng
Start Date End Date Days Time Location
1/23/2024 5/6/2024 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 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 53553
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Equivalent(s): CS 857
Cross listed with : MATH 757.01
Instructors: Marek Petrik
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 10:10am - 11:00am KING N113
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 863 (01) - Abstract Algebra II

Abstract Algebra II

Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 54709
This course extends the investigations of MATH 861 into more specialized situations related to old and new problems in mathematics, such as the nature of solutions of polynomial equations. It presents advanced properties of groups, rings, fields and their applications.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 861 with minimum grade of B-
Cross listed with : MATH 763.01
Instructors: Edward Hinson
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 9:40am - 11:00am MORR 103