Instructors:

Teacher: Itai Gurvich (e-mail: gurvich@cornell.edu)

TA: Haici Tan (e-mail: ht439@cornell.edu)

Class description

Welcome to Modeling Under Uncertainty. The name of the class captures well its essence. We will learn models (and build intuition) that will help us evaluate performance and act on it (optimize) in settings where there is uncertainty (which means, basically, almost everywhere).

The class will cover fundamental ideas and tools. Together with the optimization-methods class, these two courses serve as a useful building block for analysis in many practical context including (but not limited to) logistics and services.

Below is a rough outline of the class. We will adjust how much time we spend on each topic depending on need. Topics build on each other so that we want to feel comfortable with a topic before moving on:

0. Fundamentals: Probability via counting, axioms of probability and events, conditional probabilities and Bayes rule.

1. Static models:

a.Random variables and distributions: analytical evaluation models, and optimization.

b. Simulation, estimation and optimization.

2. Dynamic models:

a. Markov chain models (discrete and continuous), analytic results, computation and simulation.

b. Dynamic programming (optimization of random dynamics).

Class mechanics

For class structure and schedule organization (see here)

Homework: There will be a few team assignments during the semester. It is reasonable to expect one homework every couple of weeks (about 5 overall). You must submit those digitally by uploading them through canvas.

It is fine to hand-write your solution and scan them (in fact, this might be easier for you for the analytical homeworks).

For the computational homeworks (mostly in Python), the TA and I will want to see the output as well as the code.

All assignments except for the midterm assignments are in pairs (teams of two members). For most assignments, 3 to 4 hours of team‐time (after personal prep) should be sufficient. The Cornell academic integrity code stipulates that you may put your name on the submission only if you contributed to the group work. Toward the end of the term, you will be asked to fill out an assessment of teammates’ contributions to group assignments. These assessments will play a role in determining final grades.

Office hours: My office hour will be Wednesday 9:50-10:50am (immediately after class and in the same zoom address). The TA will hold office hours every Wednesday 11:00-11:50pm (https://cornell.zoom.us/j/3600078004?pwd=WWRLeE5vWDJSeGRaWGVJUDZzMzBMUT09). We will add office hours as needed. If you have question but cannot make the office hour, write to me and we will schedule a time.

Course website: You will find on this canvas site all the handouts, homeworks, solutions and practice material. The handouts contain bullet-point outlines of the class rather than detailed content. They should serve you as a guide for what we covered. I will also use handouts to clarify things when needed.

Textbook: There is no required textbook.If you wish to complement the class material, you can find some of it in the book Introduction to probability models by Sheldon M. Ross (any edition works).

I warn you, however, that I cover only a subset of the material and that book and often cover it differently than the book. However, if you wish me to point you to the place in the book where you can find specific material I am happy to do.

Grades:The breakdown of grades is as follows:

1. Final Paper/Project: 25%. I will explain more about this in class. This will be done in your teams (see item 3 below).
2. Individual midterm assignment: 15%. This is a more challenging homework that will be done individually.
3. Team homework assignments: 45%. There will be 5 homeworks that include both analytical derivations coding implementation. They will be done in pairs of your choice. You should keep the pair fixed throughout the semester.
4. Quizzes: 10%. This is basically free points. You get to repeat a quiz as many times as you'd like. The point here is to make sure you go over the material before the class. BUT you must do the quizzes to get the points.
5. Engagement: 5%. Includes discussion forum and class. This is here to make sure that if people have thoughts/questions they are posted on the discussion board for the sake of our communal learning.

All assignments except for the midterm assignments are in groups. For most assignments, 3 to 4 hours of team‐time (after personal prep) should be sufficient. The Cornell academic integrity code stipulates that you may put your name on the submission only if you contributed to the group work. Toward the end of the term, you will be asked to fill out an assessment of teammates’ contributions to group assignments. These assessments will play a role in determining final grades.

Discussion board: We will try to make good use of the discussion board on piazza (sign up to the forum atpiazza.com/cornell/fall2020/orie5530). If you have a question on material or homework, please post it there so that everyone then has access to the responses. Don't shy away from questions -- you can post anonymously (in which case only the TA and myself can see who posted and there is NO grading on the content). You are welcome also to answer each other's questions (as long as you don't solve the homework for others).

Class rules (for students and teachers)

1. Come prepared. If you did not attend the previous class, please see the video before attending. I will expect you to come prepared to the following class. This class covers a lot of ground. To get the most out of it, it is crucial that you come prepared. 
2. Participate. Be active and ask question when thing are unclear or if you have a useful comment. If you have a question, it is highly likely that others in class have the same question. We will all learn better if questioned are asked. I will also use sometimes breakout room for people to work in teams. Contributing to the team work is the best way to learn. 
3. Be on time. I acknowledge that people can be late due to unavoidable circumstances. If you are late, make sure you join with minimal disturbance. The default in zoom will be to mute upon entry. Feel free to unmute yourself to ask questions. 
4. No Homework Extensions. No extensions will be given for any homework, but we will drop the lowest homework grade at the end of the semester.
5. We will return homeworks in a timely manner. We commit to returning homeworks to you at most two weeks after the submission date. We will post the full solution before or on the date we return the grades.
6. Appeal on HW grades: The grades will be posted as will the solutions. You will have one week to request a re-grading. Notice that a re-grading is a re-grading of the whole exam. We understand that we sometimes make mistakes in grading. If you want your work to be re-graded, get back to us no later than 1 week after receiving your graded work.

Important dates

Final Project Due: December 15th


Academic Integrity: Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. The policy can be found on the university’s website here: https://theuniversityfaculty.cornell.edu/academic-integrity/. For this course, collaboration is allowed in the homework submission as indicated above.

You are encouraged to study together and to discuss information and concepts covered in lecture and the sections with other students. You can give "consulting" help to or receive "consulting" help from such students. However, this permissible cooperation should never involve one student having possession of a copy of all or part of work done by someone else, in the form of an e-mail, an e-mail attachment file, a diskette, or a hard copy.

Should copying occur, both the student who copied work from another student and the student who gave material to be copied will both automatically receive a zero for the assignment. Penalty for violation of this Code can also be extended to include failure of the course and University disciplinary action.

During examinations, you must do your own work. Talking or discussion is not permitted during the examinations, nor may you compare papers, copy from others, or collaborate in any way. Any collaborative behavior during the examinations will result in failure of the exam, and may lead to failure of the course and University disciplinary action.

Students with Disabilities: Your access in this course is important. Please give me your Student Disability Services (SDS) accommodation letter early in the semester so that we have adequate time to arrange your approved academic accommodations. If you need an immediate accommodation for equal access, please speak with me after class or send an email message to me and/or SDS at sds_cu@cornell.edu. If the need arises for additional accommodations during the semester, please contact SDS. You may also feel free to speak with Student Services at Cornell Tech who will connect you with the university SDS office.