OR/MA/ST706 Syllabus - The Fuzzy-Neural Group

OR/MA/ST 706: Syllabus

Fall 2024

Time

Tu, Th 01:30 PM – 2:45 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 706 classroom content folder at: <here>

* 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

4141 Fitts-Woolard Hall

Instructor

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

Office
4341 Fitts-Woolard Hall
919.515.2192

Office Hours
Tu, Th 3:00 PM – 4:00 PM (or by online appointment)

Teaching Assistant

Lesheng Wang (lwang65@ncsu.edu)

Office

4333 Fitts-Woolard Hall

Office Hours

Mon, Wed 11:30 AM – 12:30 PM at FWH 4333 (or by online appointment)

Prerequisite

OR/MA/ST 505 (Linear Programming) or equivalent
(Self-learning) Programming using CPlex, Gurobi, CVX, SeDuMi on MATLAB or Python.

Course Objective

OR/MA/ST 706 is a graduate-level course that prepares students to understand the basics
of nonlinear optimization. It involves mathematical analysis, algorithm design and practical
methods. It also intends to get students exposed to modern machine learning and artificial
intelligence. It prepares students for conducting research and applications in optimization
and learning. For OR students, it is a selected subject for PhD Qualifying Exams.

Course Content:

Introduction to nonlinear optimization and machine learning.
Unconstrained optimization
- Basic properties and optimality conditions
  - First order information
  - Second order information
- Solution methods
Constrained optimization
- Basic properties and KKT optimality conditions
- Lagrange dual problem
- Sensitivity analysis
- Solution methods
Applications to machine learning
- Multi-layer Neural Networks (NN) for deep learning
- Support Vector Machines (SVM) for supervised learning
- Support Vector Regression (SVR)
- Clustering for unsupervised learning
Possible extended topics (If time permits)
- Semidefinite Programming (SDP)
- Second Order Cone Programming (SOCP)

Grades

6 regular homework assignments (30%)
2 small hand-on exercises on machine learning (30%)
2 exams (unconstrained optimization 20%, constrained optimization 20%)

Evaluation Standard

A – 85 and above
B – 70 to 84
C – 60 to 69
Fail – under 60

Homework/Exercises

Homework Assignments (6)
- Weekly or biweekly
- Individual
Hand-on exercises on machine learning (2)
- Report and presentation
- At most 2 persons a team
Rule 1: No late homework/exercises without TA’s approval.
Rule 2: Turn in your homework/exercises through email to the TA and copy to Dr. Fang.
Rule 3: Convince TA for any grade changes.

Exams

Exam I: Up to unconstrained optimization.
Exam II: Up to constrained optimization.
Rule 4: No make-up exam without the instructor’s approval.

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 login using their Unity ID and complete evaluations. All evaluations are confidential; instructors will never know how any one 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.