STATS 606
Syllabus
Course description
STATS 606: Computation and Optimization Methods in Statistics is an introduction to mathematical optimization with emphasis on theory and algorithms relevant to statistical practice. At a high-level, the course consists of three parts:
- convex optimization,
- first-order methods,
- perturbation analysis of optimization problems.
If we have time at the end of the semester, we may also cover some advanced topics (e.g. non-convex optimization, online learning etc.). This is not a course on optimization theory; the goal of the course is proficiency with common scientific computing and optimization methods in statistics and data science.
Prerequisites: You should have a good grasp of vector calculus (at the level of MATH 215), linear algebra (at the level of MATH 217) and intermediate (non-measure theoretic) probability (at the level of STATS 510). We shall review relevant concepts as they arise, but this should not be the first time you see them.
References and textbooks
The first part of the course (on convex optimization basics) and the lectures on duality are based on the Ch 2–5 of Boyd and Vandenberghe’s book on convex optimization. There is no textbook for the other parts of the course; see the schedule for relevant references.
Course staff
Gang Qiao
Grading
Your grade is determined by your overall score:
- 50% problem sets,
- 50% project.
Students who obtain an average of at least 90%, 80%, and 70% will receive grades of at least A-, B-, and C- respectively. We may lower the cutoffs at the end of the semester, but we will not raise them.
Problem sets
- Problem sets are assigned weekly on Fridays and due at noon ET the following Friday. If you need an extension on a problem set, you must contact the course staff at least 24 hours before the due date. We do NOT accept late problem sets.
- Some problem sets include a problem that checks for completion of the previous week’s lab. This problem is graded like the other problems in the problem set.
- You must typeset your solutions with LaTeX. If you submit handwritten solutions to a problem, 1 pt will be deducted from your score for the problem. To help you get started, we shall make the LaTeX markup of the problem sets available. If you are new to LaTeX, we recommend editing LaTeX documents on Overleaf.
- If you are new to LaTeX, the Overleaf documentation is a great place to start. In fact, when you search for LaTeX related queries, the Overleaf documentation is often one of the first results that come up!
- Problems are graded on a scale of 1 to 4: 4 pts = essentially correct, 3 pts = minor mistakes (solution is qualitatively correct), 2 pts = some mistakes but on the right track, 1 pt = FUBAR. Your grade on a problem set is the average of your grades on the problems.
- You are encouraged to collaborate on problem sets with classmates, but the final write-up (including any code) must be your own.
Keeping up with the course online
We strongly suggest you take the course in-person, but the course is set up so that you can keep up online if necessary. Most course material is available on the course website, so please check it regularly for updates. You can also
- view lecture recordings on Canvas/YouTube,
- attend virtual office hours on Zoom (by appointment only),
- submit assignments on Canvas.
Academic misconduct
The College of LSA prohibits all forms of academic dishonesty and misconduct. Minor infractions usually result in a zero on the assignment and a one letter grade reduction; more serious or repeated infractions will result in a failing grade and additional sanctions imposed by the Office of the Assistant Dean. For more information, including examples of behaviors that are considered academic misconduct and potential sanctions, please see LSA’s Community Standards of Academic Integrity.
Accommodations for students with disabilities
We work with Office of Services for Students with Disabilities to determine appropriate accommodations on an individual basis. Please follow the instructions on their website to request accommodations.