ISE 789/OR 791 Syllabus

ISE 789/OR 791 Syllabus

Spring 2022

Time

Tu, Th 11:45 AM – 1:00 PM

* DELTA Classroom System will automatically record our lectures at the published class starting and ending times and on the published class days. Registered students can view the recorded lectures in our ISE 789/OR 791 classroom content folder at: <https://ncsu.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=258ed81c-a9d3-45c4-8d7a-ae0000c121df>

* DELTA also provides a synchronized video service with a 40-second delay at the same link to enable you attending the class online.

* If a student opens the course folder before the start time of the webcast, then they will need to refresh their browser and select the title of the webcast. The webcast will then open in a new tab. Because of the 40 second delay, the following message will appear: “This page will update once the webcast begins”. After 40 seconds the webcast will begin.
* If a student opens an individual webcast link and attempts to view it before the webcast start time then the following message will appear: “This page will update once the webcast begins”. After 40 seconds the webcast will begin.

Room

2341 Fitts-Woolard Hall

Instructor

Dr. S.-C. Fang (fang@ncsu.edu)

  • Office: 4341 Fitts-Woolard Hall
  • Office Hours: 

Teaching Assistant

TBA

  • Office: 
  • Office Hours: 

Prerequisite

  1. ISE/OR/MA 505: Linear Programming
  2. Programming using CPlex, Gurobi, CVX, SeDuMion MATLAB or equivalent

     (OR/MA 706: Nonlinear Programming or ISE/OR 708: Integer Programming will help.)

Course Objectives

  1. This graduate-level course intends to provide the fundamental theory and solution methods of commonly used optimization models for analytic system decision-making with dynamic data.
  2. It involves mathematical modeling, analytic methods, machine learning, and data-driven business decision-making.
  3. It is a flexibly structured experimental course that will evolve into a regular course.

Course Content

  1. Optimization models
    1. Linear Optimization
    2. Quadratic Optimization
    3.  Integer Optimization
    4. Linear Conic Optimization
    5. Nonlinear Optimization
  2. Machine learning approaches
    1. Supervised learning for classification and prediction
      Support Vector Machines & Regression (SVM & SVR)
      Artificial Neural Networks (ANN)
    2.  Unsupervised learning for clustering and featuring
      Similarity Learning and Sparse Solutions
    3. Reinforcement learning in a dynamic environment
      Markov Decision Process and Dynamic Programming
      (ISE 789-II Stochastic Models for Systems Analytics – Prof. Yunan Liu in Spring 2021)
  3. Systems and data
    1. Knowledge systems
      -collaborative filtering (Jester, MovieLens datasets)
      -classification (UCI repository)
    2. Financial investment systems
      -Markowitz mean-variance portfolio selection
      (S&P’s Compustat North America database)
    3. Electric systems
      -data compression and sparse signal recovery
      (Sparco database)
    4.  Power systems
      -load forecasting (GEFCom2012)
      -unit commitment (MATPOWER)
    5. Aviation systems
      -failure prediction (NASA aircraft engine dataset)

Homework

There will be 5 assignments biweekly.

Project

  • End-of-semester report and presentation.
  • At most 2 persons a team.

Exam

A comprehensive take-home exam will be given after finishing Part II.

Grades

  • Homework -50%
  • Course project -30%
  • Exam -20%

Evaluation Standard

  • A -88 and above
  • B -75 to 87
  • C -60 to 74
  • Fail -under 60

Classroom Rules

  • Rule 1: No late homework without pre-approval.
  • Rule 2: Turn in your homework through email.
  • Rule 3: Office hours can be held through Google Meet.
  • Rule 4: No make-up exam without pre-approval or an official “doctor’s note”.

End-of-Semester Class Evaluation

Online class evaluations will be available for students to complete during the last two weeks of class. Students will receive an email message directing them to a website where they can log in using their Unity ID and complete evaluations. All evaluations are confidential; instructors will never know how anyone student responded to any question, and students will never know the ratings for any particular instructors.

Evaluation Website: https://classeval.ncsu.edu

Student help desk: classeval@ncsu.edu

More information about ClassEval: http://www2.acs.ncsu.edu/UPA/classeval/index.htm

Academic Integrity

A student is expected to know what constitutes academic misconduct found in the Code of Student Conduct Policy ( POL11.35.1), and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor.

Students with Disabilities

North Carolina State University retains authority, through the Disability Services Office (located in Student Health Services Building, Suite 2221), in determining appropriate accommodations after giving consideration to the preferences of the student, the documentation provided, and institutional expertise in working with students with disabilities. If you require academic accommodations to lessen the impact of your disability, please register with the Disability Services Office at the beginning of each academic term.