Course Syllabus

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Important Links

Slack:  cse146-f20.slack.com

Weekly Schedule (subject to change)

Day Item Time Location
Monday Discussion Section   Noon - 1pm Zoom Link
Monday Office Hours (UG TAs) 2pm-4pm Zoom Link
Tuesday Class 11:40am - 1:15 pm Zoom Link
Tuesday Office Hours (UG TAs) 3pm-5pm Zoom Link
Wednesday Office Hours (Sabya)
 Noon - 1:30 pm Zoom Link
Thursday Class 11:40am - 1:15 pm Zoom Link
Thursday Office Hours (Lise)
 2pm - 3:30 pm Zoom Link
Friday Free Form Discussion
 2:30pm - 3:30pm Zoom Link

 

Class: Tuesdays & Thursdays, 11:40-1:15pm

Instructor: Prof. Lise Getoor (getoor@ucsc.edu)

TA: Sabyasachi (Sabya) Basu (sbasu3@ucsc.edu)

Undergrad Tutors & Readers

  • Fahed Abudayyeh
  • Spencer Gurley
  • Kartik Panchal
  • Zainub Sheikh
  • Andrew Thach

Course Description

  • This course provides an introduction to data-driven and algorithmic decision making, and ethical frameworks for evaluating automated systems.  There will be an emphasis on algorithmic literacy, critical analysis, and fundamental limitations of automated decision making.   We will cover concepts including predictive and causal modeling.   We will discuss bias, fairness, interpretability, privacy, and accountability.   Finally we will discuss notions of autonomy and algorithmic auditing.
  • Prerequisites:
  • Basic mathematical, statistical and programming skills are required. 
  • CSE 101, Algorithms and Abstract Data Types  and CSE 107 Probability and Statistics for Engineers or STAT 131 (or equivalent) are required.
  • CSE 142, Introduction to Machine Learning and CSE 140, Introduction to Artificial Intelligence are desirable but not required.

Topics Covered

  • Algorithmic decision making, Ethics, Causal Modeling, Privacy, Fairness, Explanation & Interpretability, Autonomy & Accountability, and other topics as time permits.

Textbook

Grading

Item Weight
Class Participation + In-Class Exercises 5%
Worksheets 10%
Quizzes 35%
Labs 25%
Projects 25%

Course Participation And Course Etiquette

  • Students are expected to attend lectures (see notes on excused absences below). There will be occasional in-class exercises; participation in class discussions and online discussions is encouraged.

Modules

  • The course will be structured around learning modules. Most modules will have an associated worksheet (aka homework), lab (aka programming assignment) and quiz.  The in/out dates are listed on the schedule and on the calendar in Canvas (these are approximate, and maybe be updated as the course proceeds)
  • Worksheet: Worksheets are typically due 5PM on Mondays.  You may submit worksheets multiple times.  No late worksheets accepted.  You may collaborate, but not copy on worksheets.
  • Quiz: Quizzes are in class, on canvas, 20 minutes, typically the day after the worksheet is due.   You may NOT collaborate on quizzes.  Quizzes will be monitored, you will need to turn on your camera, and your cannot navigate away from the quiz page (if you do, it is considered cheating).  Students will each have different copies of the quiz.  We aim to make it easier for you (and certainly more beneficial for you!) to learn the material, rather than copy!
  • Lab: Many of the modules will have labs associated with them, in which you will have the opportunity to get hands-on experience with both ML toolkits and course concepts.  Labs will use python, Jupyter notebooks, and different ML toolkits.
  • Project: Recognizing both the value and challenges of doing projects in an online setting, this quarter there will be two mini projects.  One will be a social analysis project, and will be done in the first half of the class.   The other will be a technical analysis project, and will be done in the second half of the class.   More details will be provided in class.

Late Assignments

  • No late worksheets are accepted.   Worksheets may attempted multiple times, so please submit something before the deadline.   The lowest worksheet grade will be dropped.
  • Please make sure you attend class and take quizzes on the days they are scheduled,  no makeup quizzes will be given.   The lowest quiz grade will be dropped.

    Unless otherwise stated, labs are due electronically at 5:00pm on their respective due dates.
  • Recognizing that students may face unusual circumstances and require some flexibility in the course of the quarter, each student will have a total of four free late (calendar) days to use as they see fit. NO additional individual extensions will be given.
  • Once these late days are exhausted, any assignment turned in late will be penalized at the rate of 25% per late day (or fraction thereof). Under no circumstances will a project be accepted more than four days after its due date.

Re-Grading Issues

  • The majority of the grading will be done by the TAs. If you think there has been a mistake in grading, please submit a regrade request explaining in writing, precisely and concisely, the grading error that has occurred, to the TA. Such request must be made no later than 1 week after the material in question was returned. Any request to have an assignment regraded may result in the entire assignment in question being regraded, possibly resulting in a loss of points.

Academic Integrity

Excused Absences

  • Any student who needs to be excused for a prolonged absence (2 or more consecutive class meetings), or an exam must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. No diagnostic information shall be given. Excused absences do not extend your 4 late day budget.
  • Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Disability Resource Center (DRC) within the first two weeks of the quarter.
  • Any student who must miss a class due to religious holidays should also notify the instructor during the first two weeks of class.

Diversity and Inclusion Statement

UC Santa Cruz is committed to creating an academic environment that supports its diverse student body. If you are a student with a disability who requires accommodations to achieve equal access in this course, please submit your Accommodation Authorization Letter from the Disability Resource Center (DRC) to me privately after class, during my office hours or by appointment, within the first two weeks of the quarter. Students who may benefit from learning more about DRC services can contact DRC by phone at 831-459-2089, or by email at drc@ucsc.edu.

Course Summary:

Date Details Due