Timeroom: Spring 2024

Displaying 271 - 280 of 1286 Results for: Level = All Graduate
Durham   Paul College of Business&Econ :: Decision Sciences

DS 808 (01) - Optimization Methods II

Optimization Methods II

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53434
This course introduces students to more advanced concepts and modeling techniques in mathematical programming. Topics to be covered include integer programming, nonlinear programming, multi-objective optimization, goal programming, and Monte Carlo simulation. Application areas include Marketing (e.g., pricing and revenue optimization), Finance (capital budgeting and portfolio optimization), and Operations management (e.g., production and inventory planning, shipping and distribution, routing, location selection, etc.). The course delivery will be a mix of lectures, hands-on problem solving, and case discussions.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 806
Instructors: Alison Chen
Start Date End Date Days Time Location
3/25/2024 5/17/2024 M 5:40pm - 9:00pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 808 (02) - Optimization Methods II

Optimization Methods II

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56835
This course introduces students to more advanced concepts and modeling techniques in mathematical programming. Topics to be covered include integer programming, nonlinear programming, multi-objective optimization, goal programming, and Monte Carlo simulation. Application areas include Marketing (e.g., pricing and revenue optimization), Finance (capital budgeting and portfolio optimization), and Operations management (e.g., production and inventory planning, shipping and distribution, routing, location selection, etc.). The course delivery will be a mix of lectures, hands-on problem solving, and case discussions.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 806
Instructors: Alison Chen
Start Date End Date Days Time Location
3/25/2024 5/17/2024 T 2:10pm - 5:30pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 809 (01) - Time Series Analysis

Time Series Analysis

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 53435
The course is designed to introduce analytical techniques needed in the estimation and analysis of temporal (time series) data in various business disciplines. The course focuses on theoretical and application aspects of stationary/non-stationary univariate as well as multivariate time series models. Emphasis will be given to topics such as time series regression, random walks, ARIMA/SARIMA processes, ARCH/GARCH for modeling conditional volatility, Vector ARMA models, and transfer functions. Modern software implementation is fully integrated into the course. Some examples of the business application areas include demand forecasting, financial asset return modeling, stochastic volatility modeling of financial indexes and securities, mortgage default risk assessment, online webpage click-rate modeling, market share modeling.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 803
Instructors: David Reynolds
Start Date End Date Days Time Location
1/23/2024 3/15/2024 T 5:40pm - 9:00pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 809 (02) - Time Series Analysis

Time Series Analysis

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 56836
The course is designed to introduce analytical techniques needed in the estimation and analysis of temporal (time series) data in various business disciplines. The course focuses on theoretical and application aspects of stationary/non-stationary univariate as well as multivariate time series models. Emphasis will be given to topics such as time series regression, random walks, ARIMA/SARIMA processes, ARCH/GARCH for modeling conditional volatility, Vector ARMA models, and transfer functions. Modern software implementation is fully integrated into the course. Some examples of the business application areas include demand forecasting, financial asset return modeling, stochastic volatility modeling of financial indexes and securities, mortgage default risk assessment, online webpage click-rate modeling, market share modeling.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 803
Instructors: David Reynolds
Start Date End Date Days Time Location
1/23/2024 3/15/2024 T 2:10pm - 5:30pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 810 (01) - Big Data and AI: Strategy and Analytics

Big Data

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53436
This course is designed to be a capstone experience with emphasis on the integration of materials covered in prior courses. In addition, the course provides students with the knowledge and skills to manage and model vast quantities of data for business analytics. The course covers deep neural networks and large-scale data processing using ecosystems of computing tools such as TensorFlow and Apache Spark. Students learn how to store, analyze, and derive insights from large-scale datasets and develop an understanding of the implications of deep learning for business. As a part of the capstone experience, students complete a team project that focuses on using big data and artificial intelligence for business insights, and present and discuss their work.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 801 and DS 804 and DS 805
Start Date End Date Days Time Location
3/25/2024 5/17/2024 W 5:40pm - 9:00pm PCBE 235
Durham   Paul College of Business&Econ :: Decision Sciences

DS 810 (02) - Big Data and AI: Strategy and Analytics

Big Data

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56837
This course is designed to be a capstone experience with emphasis on the integration of materials covered in prior courses. In addition, the course provides students with the knowledge and skills to manage and model vast quantities of data for business analytics. The course covers deep neural networks and large-scale data processing using ecosystems of computing tools such as TensorFlow and Apache Spark. Students learn how to store, analyze, and derive insights from large-scale datasets and develop an understanding of the implications of deep learning for business. As a part of the capstone experience, students complete a team project that focuses on using big data and artificial intelligence for business insights, and present and discuss their work.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 801 and DS 804 and DS 805
Start Date End Date Days Time Location
3/25/2024 5/17/2024 R 5:40pm - 9:00pm PCBE 215
Durham   Paul College of Business&Econ :: Decision Sciences

DS 898 (01) - Topics in Business Analytics

Top/Ped Analy: Regress Model

Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 54134
Special Topics; may be repeated. Pre- and co-requisite courses vary. Please consult time and room schedule for the specific 898 topics section you are interested in for details.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 12 credits.
Cross listed with : DS 772.01
Start Date End Date Days Time Location
1/23/2024 5/6/2024 T 5:10pm - 8:00pm PCBE 185
Final Exam 5/14/2024 5/14/2024 T 6:00pm - 8:00pm PCBE 185
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 817 (01) - Introduction to Digital Image Processing

Intro Digital Image Processing

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 56785
Digital image representation; elements of digital processing systems; multidimensional sampling and quantization; image perception by humans, image transformations including the Fourier, the Walsh, and the Hough Transforms; image enhancement techniques including image smoothing, sharpening, histogram equalization, and pseudo color processing; image restoration fundamentals; image compression techniques, image segmentation and use of descriptors for image representation and classification. Lab.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross listed with : ECE 717.01
Instructors: Richard Messner
Start Date End Date Days Time Location
1/23/2024 5/6/2024 W 5:10pm - 7:00pm KING S222
1/23/2024 5/6/2024 MWF 10:10am - 11:00am KING N310
Final Exam 5/15/2024 5/15/2024 W 10:30am - 12:30pm KING N310
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 872 (01) - Control Systems

Control Systems

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   12  
CRN: 50599
Development of advanced control system design concepts such as Nyquist analysis, lead-lag compensation; state feedback; parameter sensitivity; controllability; observability; introduction to non-linear and modern control. Includes interactive computer-aided design and real-time digital control. (Also offered as ME 872.) Lab.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Equivalent(s): ME 872
Cross listed with : ECE 772.01
Instructors: Wayne Smith
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 11:10am - 12:00pm KING N343
1/23/2024 5/6/2024 M 9:10am - 11:00am KING S316
Final Exam 5/10/2024 5/10/2024 F 10:30am - 12:30pm KING N343
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 872 (02) - Control Systems

Control Systems

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   12  
CRN: 50600
Development of advanced control system design concepts such as Nyquist analysis, lead-lag compensation; state feedback; parameter sensitivity; controllability; observability; introduction to non-linear and modern control. Includes interactive computer-aided design and real-time digital control. (Also offered as ME 872.) Lab.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Equivalent(s): ME 872
Instructors: Wayne Smith
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 11:10am - 12:00pm KING N343
1/23/2024 5/6/2024 F 2:10pm - 4:00pm KING S316