Course welcome.

website/docs/resources/slides/lecture-welcome.md

@@ -1,7 +1,7 @@
 ---
-author: Topics in Security and Privacy Technologies (CS 839)
+author: Security and Privacy in Data Science (CS 763)
 title: Course Welcome
-date: September 05, 2018
+date: September 04, 2019
 ---
 
 # Security and Privacy
@@ -41,76 +41,142 @@ date: September 05, 2018
 - Automatically check security properties
 - Search for attacks and vulnerabilities
 
-## Our focus: four modules
+## Five modules
 1. Differential privacy
-2. Applied cryptography
+2. Adversarial machine learning
-3. Language-based security
+3. Applied cryptography
-4. Adversarial machine learning
+4. Algorithmic fairness
+5. PL and verification
+
+## This course is broad!
+- Each module could be its own course
+    - We won't be able to go super deep
+    - You will probably get lost
+- Our goal: broad survey of multiple areas
+    - Lightning tour, focus on high points
+
+>  Hope: find a few things that interest you
+
+## This course is technical!
+- Approach each topic from a rigorous point of view
+- Parts of "data science" with **provable guarantees**
+- This is not a "theory course", but...
+
+. . .
+
+![](images/there-will-be-math.png)
 
 # Differential privacy
 
+##
+
+![](images/privacy.png)
+
 ## A mathematically solid definition of privacy
 - Simple and clean formal property
 - Satisfied by many algorithms
 - Degrades gracefully under composition
 
-# Applied crypto
-
-## Computing in an untrusted world
-- Proving you know something without revealing it
-- Certifying that you did a computation correctly
-- Computing on encrypted data, without decryption
-- Computing joint answer without revealing your data
-
-# Language-based security
-
-## Ensure security by construction
-- Programming languages for security
-- Compiler checks that programs are secure
-- Information flow, privacy, cryptography, ...
-
 # Adversarial machine learning
 
+##
+
+![](images/aml.jpg)
+
 ## Manipulating ML systems
 - Crafting examples to fool ML systems
 - Messing with training data
 - Extracting training information
 
+# Applied crypto
+
+##
+
+![](images/crypto-ml.png)
+
+## Crypto in data science
+- Learning models without raw access to private data
+- Collecting analytics data privately, at scale
+- Side channels and implementation issues
+- Verifiable execution of ML models
+- Other topics (e.g., model watermarking)
+
+# Algorithmic fairness
+
+##
+
+![](images/fairness.png)
+
+## When is a program "fair"?
+- Individual and group fairness
+- Inherent tradeoffs and challenges
+- Fairness in unsupervised learning
+- Fairness and causal inference
+
+# PL and verification
+
+##
+
+![](images/pl-verif.png)
+
+## Proving correctness
+- Programming languages for security and privacy
+- Interpreting neural networks and ML models
+- Verifying properties of neural networks
+- Verifying probabilistic programs
+
 # Tedious course details
 
+## Lecture schedule
+- First ten weeks: **lectures MWF**
+    - Intensive lectures, get you up to speed
+    - M: I will present
+    - WF: You will present
+- Last five weeks: **no lectures**
+    - Intensive work on projects
+    - I will be available to meet, one-on-one
+
+> You must attend lectures and participate
+
 ## Class format
 - Three components:
     1. Paper presentations
-    2. Final project
+    2. Presentation summaries
-    3. Class participation
+    3. Final project
-- Annoucement/schedule/materials: on [website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs839/)
+- Announcement/schedule/materials: on [website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/)
-- Class mailing list: [compsci839-1-f18@lists.wisc.edu]()
+- Class mailing list: [compsci763-1-f19@lists.wisc.edu]()
 
 ## Paper presentations
-- Sign up to lead a discussion on one paper
+- In pairs, lead a discussion on group of papers
-- Suggested topic, papers, and schedule on website
+    - See website for [detailed instructions](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/presentations/jjj)
-- Before each presentation:
+    - See website for [schedule of topics](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/schedule/lectures/)
-    - I will send out brief questions
+- One week **before** presentation: meet with me
-    - Please email me brief answers
+    - Come prepared with presentation materials
+    - Run through your outline, I will give feedback
 
-> If you want advice, come talk to me!
+## Presentation summaries
+- In pairs, prepare written summary of another group
+    - See website for [detailed instructions](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/summaries/)
+    - See website for [schedule of topics](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/schedule/lectures/)
+- One week **after** presentation: send me summary
+    - I will work with you to polish report
+    - Writeups will be shared with the class
 
 ## Final project
-- Work individually or in pairs
+- In groups of three (or very rarely two)
-- Project details and suggestions on website
+- See website for [project details](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/assignments/project/)
 - Key dates:
-    - **September 19**: Pick groups and topic
+    - **September 9**: Form groups, pick topic
-    - **October 15**: Milestone 1
+    - **October 11**: Milestone 1
-    - **November 14**: Milestone 2
+    - **November 8**: Milestone 2
     - **End of class**: Final writeups and presentations
 
-> If you want advice, come talk to me!
-
 ## Todos for you
-0. Complete the course survey
+0. Complete the [course survey](https://forms.gle/NvYx3BM7HVkuzYdG6)
-1. Check out the course website
+1. Explore the [course website](https://pages.cs.wisc.edu/~justhsu/teaching/current/cs763/)
-2. Think about what paper you want to present
+2. Think about which lecture you want to present
-3. Brainstorm project topics
+3. Think about which lecture you want to summarize
+4. Brainstorm project topics
 
 # Defining privacy