Syllabus

Welcome to PIC16A! I’m Professor Phil Chodrow and I’m grateful that you’re here. Before we dive into the details, I want to lay out some guiding principles for this course:

1. I want you to succeed. The purpose of this course is for you to build a set of skills that can support you in your studies, hobbies, and careers. It is not here to “weed you out,” trick you, or discourage you. You (yes, you) can learn some cool stuff and get a good grade in this course.
2. It’s still tough out there. Although some parts of our lives might be returning to normal, other parts remain affected by the COVID-19 pandemic. My main aims in designing this course are to (a) offer you flexibility to adapt to changing circumstances and (b) encourage you to support and be supported by your classmates. Python programming is fun, and I hope that this course can be a positive part of your experience during these challenging times.
3. Your wellbeing comes first. If your wellbeing or that of a loved one comes into conflict with course obligations, I hope that you will prioritize the former. I’ve included a considerable amount of flexibility in this course. If you anticipate extended difficulties related to participation or assignments, reach out to me at the earliest opportunity. We’ll find a path that prioritizes your wellbeing while still enabling you to succeed in the course. I’ve given some examples below about some situations in which I hope you’ll reach out.
4. We’ve got your back. As the instructor, I’m available to you through multiple channels. You also have an amazing support team—your TAs, LAs, and peers are all here to help you in your learning journey.

I’m a visiting assistant professor in the Department of Mathematics at UCLA. My pronouns are he/him/his. I grew up in Virginia, did undergrad at Swarthmore College in Pennsylvania, and did my PhD (after a few years traveling and working) at MIT. Then I came here to UCLA!

I love applied math, ethical data science, Star Trek, penguins, cooking, tea, Studio Ghibli movies, traditional martial arts, and effective pedagogy.

How to Address Me

If you’re not sure, please call me “Professor Chodrow.” I usually invite Learning Assistants and student research collaborators to address me as “Phil.”

Please remember to address all your professors respectfully and according to their preferences. As argued in a recent study, many of us have harmful, gendered biases about when we use earned titles like “Dr.” or “Professor.” A small, simple thing you can do to make academia a more equitable place is to check your own potential biases. If you’re not sure, “Professor X” or “Dr. X” is always a safe choice — but even better is to just ask what your instructor prefers! My own personal preference is related to this short poem by Susan Harlan.

More FAQs

I’ve collected a bunch of FAQs about myself and various things that don’t quite fit into a course syllabus here.

Python is a powerful and intuitive programming language, used extensively throughout industry and academia alike. The first part of this course introduces core Python programming constructs, including data types, functions, and classes. The second part of this course includes several applications, with particular focus on data science, data visualization, and machine learning. We will also address topics related to the benefits and harms of coding, especially as related to the topic of algorithmic bias. This offering of PIC16A places special emphasis on collaborative and project-based learning.

Primary Learning Goals

1. Programming Fundamentals: You will use core Python data structures, functions, and object-oriented programming to solve complex problems.
2. Applications: You will use Python’s package ecosystem to approach data science problems drawn from science, economics, politics, and literature.
3. Code Quality: You will create readable, documented, and reusable code. You will compare and evaluate multiple approaches to programming tasks.
4. Programming and Society: You will think critically about the impact of programming on society at large, and evaluate both benefits and harms of code products on disparate populations.

You can also consult the provisional course schedule.

Official Course Description

Lecture, three hours; discussion, two hours. Enforced requisite: course 10A, Computer Science 31, or equivalent, with grades of C- or better. Python programming and programming with Python packages. General Python programming constructs; standard data structures, flow control, exception handling, and input and output. Object oriented programming with Python. Application programming with commonly used Python modules such as PyQt or tkinter, NumPy, SciPy, and NLTK. P/NP or letter grading.

Here is some of the stuff that you will be able to do by the end of PIC16A.

Programming Fundamentals

• Containers: You will use lists and dictionaries to manage collections of data.
• Functions: You will write reusable functions to perform complex, customized tasks.
• Control Flow: You will use branching logic and loops to structure sophisticated programs.
• Object-oriented programming: You will write custom classes and define their behavior, and use inheritance to extend their functionality.

Applications

• Modules: You will access and apply a wide variety of Python modules commonly used in software development, data analysis and computational science.
• Arrays and Vectorizaton: You will use the Numpy and Pandas packages to efficiently manipulate arrays and data frames.
• Data Visualization: You will use the Matplotlib package to create insightful and attractive data graphics.
• Machine Learning: You will deploy and evaluate machine learning algorithms on real-world data sets.
• Project: You will collaboratively complete an extended project in which you analyze and make predictions on a real scientific data set.

Code Quality

• Concision: You will eliminate redundant code components and enclose reused code within functions and manipulating arrays with vectorized tools.
• Documentation: You will carefully document your code with comments, docstrings, and surrounding text.
• Robustness: You will use exceptions to appropriately inform the user about invalid inputs, and design unit tests to ensure the correct functioning of your functions and classes.

Programming and Society

• Concepts: You will be able to define different kinds of algorithmic bias, as well as associated concepts like feedback loops.
• Analysis: You will be able to analyze existing and proposed code products and raise questions about their impacts on different groups of people.
• Replication: You will conduct data analyses to document algorithmic bias in real-world systems, study how those systems came to possess bias, and propose improvements.

Course Team

Instructor  

Phil Chodrow (he/him). Contact: phil@math.ucla.edu  

Teaching Assistant  

Hui Jin

Learning Assistants  

Jaya Ren and Jonathan Liu

Schedule

Discussion A 

TR, 9:00am-9:50am (Pacific Time), Royce Hall 156

Discussion B 

TR, 10:00am-10:50am (Pacific Time), Bunche Hall 3211

Lecture 

MWF, 9:00am-9:50am (Pacific Time), Boelter Hall 5249

Student Hours (via Zoom)

Student hours are your time to come speak with me or the TA about anything related to the course. Come ask for help about homework assignments; concepts and techniques; your project; your career; or anything else that might be on your mind.

I want you to come to Student Hours. You’ll get a small amount of extra credit the first time you come to Student Hours and ask a question.

Prof. Chodrow

Mondays at 10am PT, in person or via Zoom

 

Wednesdays at 6:30pm PT, via Zoom

Hui

TBD

If you’re not able to attend the scheduled Student Hours, please email me and we will set up a separate time.

Waitlist Policy

All students on the waitlist will be enrolled in the course at the end of Week 2. Waitlisted students are expected to keep up with all course activities and assignments. If you join the waitlist during the first two weeks, contact me to discuss make-up work.

Here’s a quick summary of what you might do in a typical week of PIC16A.

Grading is broken. Grades are used in higher education to both (a) give you feedback on how you can grow and (b) assess your “merit” or “mastery” of course content. This latter purpose is especially broken. There’s no pedagogical justification for these so-called “formative assessments.” The results are not predictive of future success or “merit,” and academics are, on average, bad at designing assessments that equitably measure meaningful competency. Professor Amy J. Ko at the University of Washington has an excellent reflection on the deep flaws of grading, which you might wish to read.

If I could drop grades and simply give you feedback on how you can grow, I would in a heartbeat. Unfortunately, the policy of the Department of Mathematics and UCLA is that I must give grades. The system below does not overcome the fundamental flaws of grading, but I hope that it can at least be less opaque and inequitable than many other systems.

Core Rubric

Homework  

30%

Participation  

15%

Quizzes  

10%

Mini-Project  

15%

Midterm Exam  

20%

Final Exam  

10%

We Are All On The Same Team

I do not grade on the basis of ranks or other distributional information. Your final letter grade in this course is a reflection of your own effort, learning, and teamwork. If every student fully meets my expectations along these axes, then I will not hesitate to give every student an A.

Concretely, your final letter grade in PIC16A is based on the following two factors:

1. Your final course average. I use the straight scale as a minimum, and reserve the right to be more generous. For example, if you achieve a final average of a 92 in the course, that guarantees you at least an A-. The only exception is the A+ grade, which is not guaranteed by any numerical average. I am very stingy with these and only give them out to students who have shown exceptional teamwork, creativity, reflection, and growth, in addition to ending the quarter with a very high average.
2. My professional assessment of your learning. This is the “extra” that could push that A- to an A. You will have an opportunity on the final exam to reflect on your time in the course and share with me any information about your learning that may not be reflected in your numerical average.

Thus, you are not competing against your peers for grades. This means that you don’t hurt your own prospects when you help your peers understand a concept. Indeed, just the opposite – when reasonably possible, I will reward exceptional collaboration.

Expected Effort

Every student will need a different amount of time each week to maintain the brisk pace of this course. That said, this is a pretty intense class, and you should be prepared to devote a considerable amount of time to it. I expect that most students will need something like 10-12 hours a week for this class, on average. That will be less in some weeks, and more in a few.

If you’re spending much longer than that, it’s ok! There’s nothing wrong, and I believe you can succeed in the course. Please reach out to me if you’re concerned about the amount of time you’re spending on this class.

Homework

There will be 7 homework assignments throughout the quarter. You should consult the coding expectations and expectations for submitted assignments for guidance on how to polish, document and submit your work.

Homework assignments can be pretty challenging in this course. Some of them might require 5 or more hours to complete. Please start early and ask for help to make sure you can complete a high-quality assignment.

The lowest homework score will be dropped. Homeworks can be submitted up to 3 days late, at a discount of 10% per 24 hours past the deadline. For example, if you turn in a homework 20 hours late that would have received a 98%, your grade will be an 88%. If you turned it in 39 hours late your grade would be a 78%, and so on. Homeworks turned in more than 3 days (72 hours) late will receive 0% unless you’ve received an extension from me.

Participation

Attendance at scheduled lecture sessions is encouraged but optional. You can achieve a 100% in this course without ever attending a scheduled lecture section. Of course, attendance at lecture will help you learn the material more rapidly and thoroughly. Scheduled lecture sessions will include Q&A, discussion, supplementary lectures, and both individual and group activities.

Attendance at Discussion sessions is expected. What “expected” means is that Discussion activities count toward your overall grade. The highest grade that can be obtained in PIC16A without attending Discussion is a B+.

Under certain circumstances, you can receive an exemption which will allow you not to attend the scheduled Discussion section. You should request an exemption from me if:

• You are in a timezone outside the U.S. that makes attendance at the scheduled times disruptive to your sleep schedule.
• You are concerned for the health of yourself or your loved ones, and therefore do not feel safe attending a course activity in person.

In either case, I will match you with a group of students in a similar situation. You will be responsible for working out an alternative meeting time over Zoom.

Discussion sections will include graded group work, including exercises and creative problem-solving. This graded work is the basis of your participation score. Your Discussion activities are graded on participation: if you’ve put in 50 minutes of solid effort, you’ll get full credit. Generally, you’ll only receive less than full credit if you didn’t attend Discussion (in which case you’ll receive a 0 for that day). The lowest three grades from Discussion assignments will be dropped. You are expected to consult the expectations for working in groups prior to the first Discussion section of the quarter.

Missing a Discussion Section

Generally speaking, if you miss a Discussion section, you can simply use one of your drops. It is still advisable to review the material, but your grade will not be affected.

If you know of a conflict with your scheduled Discussion in advance, you can contact your group. If your group unanimously agrees, you may (a) notify the TA and (b) meet outside your scheduled Discussion section that day. You will still need to submit the worksheet by the stated deadline on CCLE, which is usually 24 hours after Discussion A begins.

Quizzes

All traditional lectures will be pre-recorded and available online. Each lecture day, there will be a quiz consisting of questions related to that day’s lecture videos and required readings. The primary purpose of the quizzes is to encourage you to study the required lecture content in time to apply your knowledge in the group Discussion sections.

Quizzes are timed for 30 minutes. They are due at 10:00am on the day following lecture. For example, a typical quiz corresponding to Wednesday’s lecture might consist of 2-3 questions, and can be taken in any contiguous 30-minute period that ends before 10:00am the following Thursday.

The lowest 5 quiz grades will be dropped. Lectures, lecture notes, and required readings will always be posted at least 48 hours before the quiz is due.

Project

There will be a group project due at the end of the course. Working with your group, you will produce a small data science project in which you will read, explore, train machine learning models, and communicate your findings using a real data set. Several Discussion sections in the second half of the course will be devoted to helping you get started on the stages of this project. The project will be designed to require approximately 3-5 person-hours per group member outside of scheduled class times. It will be possible to start on the project by the end of Week 7, and you will have all the tools you need to finish the project by the end of Week 9.

Midterm Exam

The midterm exam will take place in Week 5 or Week 6. It will be released at 8am, Pacific Time on one day, and will be due 24 hours later at 7:59am, Pacific Time, on the following day. The exam is intended to require approximately ninety minutes of effort, but you are free to spend more time on it within the 24 hours. This exam is open-book and open-notes.

Final Exam

The final exam for this course will take place during the standard final exam period, at the time determined by the Registrar’s office. The final exam is not a coding exam. Instead, you’ll be asked to write very short essays on topics such as:

• Key concepts in machine learning.
• Algorithmic bias and the limitations of machine learning.
• Your role in the project, and what you learned from your experience.

This exam is not intended to be “tricky.” If you’ve been following lectures and actively participating in your group project, you should do fine.

Extra Credit

There are a few ways to earn extra credit in PIC16A, which I describe in the “Extra Credit” section below.

Masks

Mask-wearing is required for all in-person activities unless you have received an exemption from CAE.

Positive Test

I’ll be real with you: there is a fair chance that several people in this class, possibly including you or I, will test positive for COVID-19 during this quarter.

If you test positive or experience symptoms of COVID-19, please:

1. Do not come to Lecture or Discussion.
2. Follow UCLA Health’s instructions for what to do next.
3. If you’re able, send me an email to let me know that you are experiencing health issues and that you will need appropriate accommodations.
4. Listen to your body, drink lots of fluids, and rest. You’re going to be ok, and your grade in this course is going to be ok, too.

If you’ve tested positive but are feeling ok, it’s still possible to catch all course content and activities. I’ll be recording Live Lectures and posting them on CCLE – catch up with them there. Tell your group that you will need to Zoom in to Discussion. Come to Zoom Student Hours. You don’t have to miss a thing! Just don’t physically come to class until you’ve completed your quarantine. If you aren’t feeling well, then don’t push yourself. I’ll make sure you’re able to make up work without penalty.

If I test positive, I will cancel in-person Live Lectures and Student Hours. Depending on my state, I may offer online Live Lecture and Student Hours as a replacement.

The hidden curriculum refers to the implicit knowledge and habits—not usually taught explicitly—that students pick up “along the way” in their education. These often relate to asking for help, using available resources, and planning work. Often, students with college-educated parents are more comfortable in the hidden curriculum than first-generation college students. I’d like to make sure that everyone knows the following about my class.

• It’s never wrong to ask me for help.
  It is literally my job to help you succeed in this class. If at any time you’re concerned about your ability to keep up the pace, just reach out and we’ll see what we can do. I won’t always be able to give you exactly the support you request, but I will do my best. I’m more able to help you out if you approach me early, as soon as issues come up.
• Your wellbeing comes first.
  If you are experiencing circumstances that make it difficult for you to complete your work for this class—especially if those circumstances are health-related—please let me know. There are plenty of drops for Discussion and quizzes, and I will grant extensions on homework assignments with valid reasons. “I didn’t manage my time well this week” isn’t a valid reason (in this case I would suggest you use your one homework drop), but “I am sick,” “my internet is unreliable,” “I am changing housing,” etc. etc. are all appropriate.
• Student Hours are for you.
  Student Hours, often called “Office Hours” in other courses, are your time. Come by to ask questions, chat with me, or just work on homework. You don’t need a “reason” to come to Student Hours, and you shouldn’t worry about disturbing me. Again, it’s your time. I’ll be very happy to see you.
• You can ask me to advocate for you.
  This is most commonly related to letters of recommendation (see section “Advice and Letters of Recommendation”), but if there’s another way in which I can use my position to help you, let me know.
• If something is hard for you, that’s ok.
  Maybe you’re struggling on a problem. That’s good! I know it feels frustrating, but that is where learning happens. If you are having a hard time on a problem, please remember:
  • You are not the only one. I promise.
  • You are not a bad student.
  • You can still succeed in this class and in future endeavors involving programming.
  • Ask for help! You’ve got me, the TA, the LAs, and your classmates. We are all here for you.
• It’s ok—actually, it’s awesome—to collaborate with your peers on homework.
  It’s not cheating to work together on homeworks (at least in this class). Make sure to credit your collaborators at the top of your assignment, and observe the considerations in the section “Collaboration and Academic Honesty.”

To expand on one of the points above: if you are a first-gen student, I especially encourage you to reach out to me. I’ll offer you what tips I can about navigating your time at UCLA.

One of the guiding principles of this course is that your wellbeing comes first. If your wellbeing comes into conflict with the course obligations, I hope that you’ll prioritize your wellbeing and reach out to me.

• If you or someone you love is experiencing a health crisis, prioritize your wellbeing. You can use some of your drops to take a break from assignments, or you can also ask me for an extension or other accommodation.
• If some aspect of the course is causing undue stress or anxiety, feel free to let me know. I regularly make adjustments to the course to promote student mental health while still meeting course learning objectives.
• If you do not have reliable internet or other resources needed to access class resources, let me know and we’ll see what we can do.
• If you are having trouble managing your time, feel free to ask me for advice. I don’t usually grant accommodations for this reason, but I may be able to help you use your time more efficiently in the future.

These examples are not exhaustive. If you are in any situation in which you feel that your obligations to PIC16A are detrimental to your wellbeing or the wellbeing of someone you love, please consider contacting me. Please also remember that the sooner you approach me, the better I can help you.

I heartily encourage you to collaborate with your peers on homework and many in-class activities. You should ask questions, brainstorm ideas, and offer suggestions at a conceptual level. Sharing code is usually ok, but should usually be minimized. That is, you should attempt to share the smallest amount of code possible that would help you ask your question or answer someone else’s question. Additionally, you should discuss to ensure that everyone understands what’s going on! Blind copy/paste won’t help anyone learn.

The quizzes, midterm, and finals are open-book and open-notes. You should not discuss any aspect of these evaluations with your peers, and doing so will constitute a violation of UCLA’s student code of conduct.

Advice

I am always happy to talk with you about your future plans, including internships, REUs, and graduate school applications. Because I am a creature of the academy, I am less knowledgeable about industry jobs, although you are welcome to ask about those too.

Letters of Recommendation

If you have completed a course with me or are currently enrolled, you are welcome to request a letter from me. If I feel that I am not able to write you a strong letter, I will tell you – but if you still want a letter from me, I will still write it.

Please keep in mind that I can write stronger letters for students whom I see more frequently, such as in lecture or office hours. If you’d like a letter, talking to me in these contexts, or scheduling another meeting time, is highly recommended.

To request a letter, fill out this request form.! Please give me at least one month of advance notice when possible.

When I Won’t Write a Letter

As a matter of moral principle, I will not write letters of recommendation for programs or jobs involving any of the following:

• Policing (including but not limited to predictive policing, development of algorithms that predict recidivism, etc.);
• Military applications (such as internships at the Department of Defense or any of its international counterparts);
• Weapons manufacturing, broadly construed;
• Intelligence gathering (such as internships at the NSA, FBI, or any international counterpart).

I am very happy to discuss this policy with any student who has questions. Conversations about when and how mathematics, data science, and programming should be used are lacking in our community. If you’d like to engage me in such a conversation, that would be great! However, this policy is non-negotiable. Therefore, if I refuse to write you a letter on these grounds, please know that it doesn’t reflect on your ability to succeed in PIC16B, your career potential, your worth as a person, or whether I like you.

The Just Mathematics Collective has compiled a list of resources for students on making ethical career decisions, which is available here. The text of this section is lightly modified from their suggested text on letter-writing.

There are several ways to earn extra credit in PIC16A.

Essay

At any time prior to the end of instruction, you may submit an essay of between 600 and 900 words (2-3 double-spaced pages) on a topic broadly related to the themes of algorithms, equity, and justice. Your essay should address a concrete problem, assess the current state of affairs, and suggest one or more paths forward. Examples of suitable topics related to algorithms and justice include but are no means limited to bias in facial recognition, the impact of automation on social services for the poor, and the role of technology in combatting climate change.

Not sure whether your topic is appropriate? A good topic will usually include the following two components:

• Concrete benefits or harms. Examples of concrete benefits or harms include changes to one’s likelihood of being arrested, granted health services, approved for a loan, or granted reasonably-priced health insurance.
• Disparate impacts. You should show that the system in question harms or benefits certain groups more than others.

A bad example: arguing that social media is driving political polarization. This is an interesting topic, but it does not describe concrete benefits or harms, nor does it describe disparate impact.

A better example: arguing that polarization on social media is leading to an increase in hate crimes (concrete harm) toward specific marginalized groups (disparate impact).

Please note that questions of equity and justice are more specific than the general impact of social media or tech companies on society as a whole. Essays on such general topics will receive only partial credit.

You are welcome to argue any angle on your topic, provided that you can offer sound sources and argumentation. For example, you may argue that bias in facial recognition is a major problem in need of solution, or you may argue that no substantial problem exists. You are welcome to ask me about your sources and proposed argument before you begin writing.

This essay will be graded out of 3 points, and the result will be added as a percentage to your final average in the course. For instance, if your essay earns 2 points, 2% will be added to your final average. Please note that, because 3% is quite a lot of extra credit, I will tend to grade these essays quite strictly. Exceptional effort is required if you want all three points.

Your essay should satisfy the following criteria.

• Sources: The essay should cite and elaborate on at least four news articles, scholarly journal articles, or other primary sources. Opinion articles do not count. Blog posts are generally unsuitable, but posts by established experts may be permissible; ask me if you’re not sure. You should use a consistent citation convention. If you’re not sure which one to use, APA and MLA are both good choices. Up to one source may be a documentary film, while the remainder must be written.
• Argument: The essay should have a thesis statement, which is supported by the discussion of the sources. Several examples of successful thesis statements:
  • Taken together, these articles suggest that algorithmic surveillance of marginalized groups is only increasing with time.
  • Although studies have revealed significant biases in Google Search, more recent work suggests that these biases may be less important in practice than once believed.
  • While many proposals exist for how algorithms and computation can be used to combat climate change, these proposals often overlook the role of human interpretation in implementing computer-generating guidance.
• Grammar and Writing: Your essay should be written in grammatical English, with complete sentences and careful punctuation.

The Undergraduate Writing Center is an excellent resource for helping you craft high-quality essays.

Suggestions for Sources

You are free to use any sources to which you have access. If you are having trouble getting started, the following may be useful starting places.

• The Algorithmic Justice League maintains a library of articles and announcements that may help you find suitable material. Especially relevant for topics at the intersection of algorithms and racial justice.
• A list of articles on bias in algorithms. This list is curated by Safiya Umoja Noble, author of Algorithms of Oppression: How Search Engines Reinforce Racism and faculty here at UCLA.
• Cathy O’Neil, author of the excellent book Weapons of Math Destruction, has a blog. Many of her posts are opinion pieces (and therefore unsuitable for citation), but she always provides links to primary material, which you can use.

Campuswire

There is a Campuswire discussion forum associated with this course, where you can post both questions and answers about the course content. If you post a particularly insightful question or answer, I will (at my discretion) award you 0.25% of extra credit toward your final grade. You can earn extra credit this way up to four times, for a total of 1% extra credit.

Student Hours

You can get extra credit just by coming to Student Hours! The first time you attend OH and ask me a question, you will receive 0.25% extra credit.

Course Feedback

There will be several surveys throughout the quarter which allow you to give me feedback on the course. Completing these feedback surveys is worth 0.25% extra credit, each.

You are always welcome to speak with me about any experience or aspect of your life that may effect your safety, ability to learn, or ability to participate equitably in the UCLA community. I offer you the following commitments:

• I commit to welcoming and celebrating you, as you are.
• I commit to making time for you.
• I commit to listening to you actively and sympathetically.
• I commit to believing you.
• I commit to helping you find the resources that best meet your needs.
• I commit to respecting your privacy to the extent consistent with my reporting obligations under Title IX (see below).

Sexual Misconduct

• UCLA’s Sexual Violence Index, including definitions, more thorough descriptions of available resources, and suggested safety measures.
• Title IX Office.
• Office of the Dean of Students.
• UCLA Police Department (UCPD)

Confidential Resources

Campus Assault Resources & Education (CARE)

• Crisis support available 24 hours a day: (310) 206-2465

Counseling and Psychological Services (CAPS)

• Counselors available by phone 24 hours a day: (310) 825-0768

Rape Treatment Center UCLA Medical Center Santa Monica

• Support available 24 hours a day: (424) 259-7208

Student Legal Services

• Available Monday-Friday, 9am-12pm and 1pm-5pm
• $10 initial consultation fee, currently waived due to COVID-19.

My Obligations under Title IX

I am a Responsible Employee, which means that I am required by the University to report incidents of sexual harassment or sexual violence to the Title IX Coordinator. If you disclose such an incident to me, I will report it to the Title IX Coordinator. This may lead to follow-up from the Title IX office and a possible investigation into the incident. If you would like to speak to a resource who will maintain strict confidentiality, see Confidential Resources.

Mental Health

Counseling and Psychological Services (CAPS)

• Counselors available by phone 24 hours a day: (310) 825-0768

Students with Disabilities

Office for Students with Disabilities (OSD)

• Provides academic support services for students with documented permanent or temporary disabilities, at no cost to students. Example services include readers, adaptive equipment, sign language interpreters, test-taking arrangements, transportation, and much more.

UCLA Disabilities and Computing Program (DCP)

• Provides services related to accessible computing technology, including support for students and training for faculty.

Equity, Inclusion, and Access

The Office of Equity, Diversity, and Inclusion maintains a list of resources. Several highlights:

• Resources for Racial Trauma
• Resources on Immigration Policy Changes
• Resources on Native American and Indigenous Affairs

The Community Programs Office offers several student-run programs aimed at the development of an inclusive and diverse community of scholars and leaders on and off campus.

• The Student Retention Center offers several programs supporting students who may be experiencing difficulties in their academic or social lives. Specific populations served include students within the Afrikan diaspora, Raza students, Native and Indigenous students, Pilipino students, and Southeast Asian students.
• Academic Advancement Program (AAP) Peer Learning (serving AAP students).

International Students

Dashew Center for International Students and Scholars

• List of resources related to visas, arrival in the US, housing, and other important topics.

• Course Environment Statements lightly modified from Chad Topaz
• Opening statement inspired by Robert Talbert.