Timeroom: Spring 2023

Displaying 241 - 250 of 393 Results for: Attributes = EUNH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 738 (1SY) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 52532
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 Trees (CART), Random Forests, Neural Nets, Support Vector Machines, Logistics 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. Undergraduate students are required to have junior or senior status to in enroll in this course. Prereq: MATH 539 (or MATH 644); or permission.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Mutual Exclusion : IT 630
Classes not allowed in section: Freshman, Sophomore
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MW 12:40pm - 2:00pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 740 (1ON) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   75  
CRN: 51699
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. Prereq: MATH 539 (or 644); or permission.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Classes not allowed in section: Freshman, Sophomore
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/24/2023 5/8/2023 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 743 (1SY) - Time Series Analysis

Time Series Analysis

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 56258
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 Fournier transform, linear filters, parametric spectral estimation, dynamic Fournier 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 into the course. Prereq: MATH 739. Offered in alternate years in the spring semester.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Classes not allowed in section: Freshman, Sophomore
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MWF 9:40am - 11:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 756 (1SY) - Principles of Statistical Inference

Princpls Statistical Inference

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 50551
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. Prereq: MATH 755; or permission.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Classes not allowed in section: Freshman, Sophomore
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MWF 11:10am - 12:30pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 836 (1SY) - Advanced Statistical Modeling

Advanced Statistical Modeling

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 54611
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: Qi Zhang
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MWF 8:10am - 9:30am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 838 (1SY) - 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 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 52533
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: Philip Ramsey
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MW 12:40pm - 2:00pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 840 (1ON) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 51700
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.
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/24/2023 5/8/2023 Hours Arranged ONLINE
Additional Course Details: 

I am providing a syllabus for the course that references Fall semester 2022, however the course in Spring 2023 follows the same exact format and has the same content.

Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 843 (1SY) - Time Series Analysis

Time Series Analysis

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 56260
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.
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: Linyuan Li
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MWF 9:40am - 11:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 856 (1SY) - Principles of Statistical Inference

Princpls Statistical Inference

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 50552
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-
Instructors: Linyuan Li
Start Date End Date Days Time Location
1/24/2023 5/8/2023 MWF 11:10am - 12:30pm ONLINE
Manchester   Liberal Arts :: Music

MUSI 403 (M1) - Roots of Rock

Roots of Rock

Online Course Delivery Method: Online with some campus visits, EUNH
Credits: 4.0
Term: Spring 2023 - UNHM Credit (15 weeks) (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   18  
CRN: 56540
Focuses on the musical styles, traditions, and social circumstances that led to a distinctive form of American popular music in the 1950's and '60's. In addition to developing critical listening skills to discern subtle distinctions among such styles and sub-styles as blues, folk, jazz, and country, the course also considers the diverse social trends that helped drive changes and developments in the various styles and genres covered. While some attention will be devoted to rock music of the mid-late sixties, the course emphasizes the various musical styles that preceded rock.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Attributes: Fine&PerformingArts(Discovery)
Instructors: David Price
Start Date End Date Days Time Location
1/24/2023 5/8/2023 Hours Arranged ONLINE
1/24/2023 5/8/2023 M 1:01pm - 3:50pm PANDRA P126