cs763/website/docs/org.md

Lectures will be loosely organized around three core modules: differential
privacy, adversarial machine learning, and applied cryptography. We will also
cover two advanced modules: algorithmic fairness, and PL and verification
techniques.  

This is a graduate seminar, so not all lectures are set in stone and there is
considerable flexibility in the material. If you are interested in something not
covered in the syllabus, please let me know!

## Course Materials

For differential privacy, we will use the textbook *Algorithmic Foundations of
Data Privacy* (AFDP) by Cynthia Dwork and Aaron Roth, available
[here](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).

## Grading and Evaluation

Grades will be assigned as follows:

- **Paper presentations: 25%**
- **Homeworks: 15%**
- **Final project: 60%** (Milestones 1 and 2, and final writeup)

These three components are detailed below.

### Paper presentations

**Paper discussions** are one of the main components of this course. In groups
of two (or very rarely three), you will present 2-3 papers on a related topic
and lead the discussion; we will have presentations most Wednesdays and Fridays.
Your presentation should last about **60 minutes** long, leaving the remainder
of the time for a wrap-up discussion. Please sign up for a slot and a paper by
**Monday, September 9**; while we will try to accommodate everyone's interests,
we may need to adjust the selections for better balance and coverage.

Before every presentation, all students are expected to read the papers closely
and understand their significance, including (a) the main problems, (b) the
primary contributions, and (c) how the technical solution. Of course, you are
also expected to attend discussions and actively participate in the discussion.

We will be reading about topics from the recent research literature. Most
research papers focus on a very narrow topic and are written for a very specific
technical audience. It also doesn't help that researchers are generally not the
clearest writers, though there are certainly exceptions. These
[notes](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) by
Srinivasan Keshav may help you get more out of reading papers.

### Homeworks

There will be three small homework assignments, one for each of the core
modules, where you will play with software implementations of the methods we
cover in class. These assignments will be lightly graded; the goal is to give
you a chance to write some code and run some experiments.

### Course Project

The main course component is the **course project**. You will work individually
or in pairs on a topic of your choice, producing a conference-style write-up and
presenting the project at the end of the semester. The best projects may
eventually lead to a research paper or survey. Details can be found
[here](assignments/project.md).

## Learning Outcomes

By the end of this course, you should be able to...

- Summarize the basic concepts in differential privacy, applied cryptography,
  and adversarial machine learning.
- Use techniques from differential privacy to design privacy-preserving data
  analyses.
- Grasp the high-level concepts from research literature on the main course
  topics.
- Present and lead a discussion on recent research results.
- Carry out an in-depth exploration of one topic in the form of a self-directed
  research project.

## Credit Information

This is a **3-credit** graduate seminar. For the first 10 weeks of the fall
semester, we will meet for three 75-minute class periods each week. You should
expect to work on course learning activities for about 3 hours out of classroom
for each hour of class.

## Academic Integrity

The final project may be done in groups of three (or in rare situations, two)
students. Collaborative projects with people outside the class may be allowed,
but check with me first. Everything else you turn in---from homework assignments
to discussion questions---should be **your own work**. Concretely: you may
discuss together, but **you must write up solutions entirely on your own,
without any records of the discussion (physical, digital, or otherwise)**.

## Access and Accommodation

The University of Wisconsin-Madison supports the right of all enrolled students
to a full and equal educational opportunity. The Americans with Disabilities Act
(ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document
1071) require that students with disabilities be reasonably accommodated in
instruction and campus life. Reasonable accommodations for students with
disabilities is a shared faculty and student responsibility. Students are
expected to inform me of their need for instructional accommodations by the end
of the third week of the semester, or as soon as possible after a disability has
been incurred or recognized. I will work either directly with you or in
coordination with the McBurney Center to identify and provide reasonable
instructional accommodations. Disability information, including instructional
accommodations as part of a student’s educational record, is confidential and
protected under FERPA.