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📖 Syllabus

Table of Contents

  1. About 🧐
  2. Communication 💬
  3. Technology 💻
  4. Course Structure 🍎
    1. Lecture
      1. Pre-Recorded Lectures
      2. Lecture Class Time
    2. Discussion
    3. Homework
    4. Office Hours
  5. Exams 🧪
    1. Redemption Policy
  6. Policies ✏️
    1. Grading
    2. Late Policy, Slip Days, and Drops
    3. Regrade Requests
    4. Incomplete Grades
    5. Academic Integrity
    6. A note on letter grades
  7. Support 🤝
    1. Accommodations
    2. Diversity and Inclusion
  8. Acknowledgements 🙏

About 🧐

The world is increasingly recognizing the value of data when solving complex and open-ended problems. In the past, we might have explicitly told the computer exactly how to differentiate between the letters of the alphabet, but today we instead give the computer many examples of each letter and let it learn the differences automatically. It is a fascinating time to be working with data! The next obvious question then is… “But how do we learn from data?”

This is the central question of DSC 40A. We will see that virtually every rigorous learning method involves two steps: i. turning the abstract problem of learning into a concrete math problem; and ii. solving that math problem. This quarter, we will see how to apply this fundamental approach in a variety of contexts. After this class, you will understand the basic theoretical principles underlying almost every machine learning and data science method — from simple linear regression to deep neural networks. You’ll also be better prepared to tackle the math you’ll see in your upper-division courses, like vector calculus, linear algebra, and probability.

Communication 💬

This quarter, we’ll be using Ed as our course message board. You should be added to Ed automatically; if not, a link will be provided in class. Please join right away as we’ll be making all course announcements through Ed.

If you have a question about anything to do with the course — if you’re stuck on a problem, didn’t understand something from lecture, want clarification on course logistics, or just have a general question about data science — please make a post on Ed. If your question is about an active HW problem, please make your post private so that others cannot see it and include your thoughts, parts of an answer (even if you are unsure if it is correct), or what steps you have tried.

Course staff will regularly check Ed to answer questions. You’re also encouraged to answer questions asked by other students. Explaining something is a great way to solidify your understanding of it!

Please don’t email staff members (and don’t message them on social media); just make a private or public Ed post instead! I will not answer emails regarding course material/logistics, etc.

Technology 💻

We will be using several websites this quarter. Here’s what they’re all used for:

  • Course Website: Where all content will be posted.
  • Ed: Discussion forum for announcements and communication.
  • Gradescope: Platform for submitting assignments and viewing grades. You should be automatically added to Gradescope.
  • Google Colab: Google’s shareable live Jupyter notebook web application for any coding demos or projects.

Note that we WILL NOT be using Canvas at all. I only have Canvas as a way to auto-enroll you in Gradescope and to submit your grades to UCSD’s lovely egrades system.

Course Structure 🍎

This course will include pre-recorded lectures, live demos, live worked problems, group work (discussion) sessions sections, plenty of office hours, and weekly homework assignments. Oh Joy!


Pre-Recorded Lectures

Lectures are pre-recorded. Podcasting is not perfect, and with pre-recorded lectures, students can view a higher quality recording at their own speed and pace. I have tried to talk slowly, so you may want to watch at 1.2x speed. Lectures will cover the theoretical aspects of the material. Any questions you have on lecture material, please post them on Ed.

Lecture Class Time

We have found that students like to have as many example problems worked out for them in this class. So, I have turned the scheduled lecture time into an interactive problem-solving session. We will work on practice problems, that are more difficult than exam problems, step-by-step together. We will also work through several code demos to provide context and examples of the theory.

Lecture is not required, but highly recommended and incentivized with extra credit opportunities


Discussion sections will be primarily used to facilitate problem-solving in small groups with peers.

We will provide a worksheet of problems, which you will complete in a group of two to four students. The group work should help reinforce concepts from the lecture and further prepare you for homework.

Attendance at discussion section is required for full credit on the group work, but if you cannot attend, you may complete the group work worksheet in a self-organized group of two to four students outside of the discussion section for 80 percent credit.

If you have specific people in your section that you want to work with, you may work together, otherwise, the tutors in discussion will pair you with other students.

Only one member of each group should submit the worksheet, and they should indicate the names of all group members on Gradescope. Worksheets won’t be graded on correctness, but rather on good-faith effort. Even if you don’t solve any of the problems, you should include some explanation of what you thought about and discussed, so that you can get credit for spending time on the assignment. In order to receive credit, you must work in a group of two to four students for at least 50 minutes. You may not do the group work alone.


This class will have weekly homework assignments.

Homework should be written or typed (using LaTeX) up and turned in by each student individually. If you want to type up your answers, we recommend using markdown with LaTeX or

You may talk to other students in the class about the problems and discuss solution strategies, but you should not share any written communication. You can tell someone how to do a homework problem, but you cannot show them how to do it. One way to tell if you are respecting this boundary is to ask yourself whether your collaboration could take place over the phone. Additionally, the content of your verbal communication should involve the problem-solving strategy and approach, and you should not directly compare answers with classmates.

Talking through homework problems with other students can be very valuable for many reasons:

  • You will learn about someone else’s thought process and new ways of solving problems that you may not have thought of.
  • You will get practice explaining your ideas, which is a useful life skill, and important for job interviews.
  • You will get practice thinking critically about whether someone’s proposed solution actually works, and you will learn how to poke holes in shaky arguments.

As a result of this collaboration policy, students may have similar approaches to problems, but they should not have similarly presented solutions, such as word choice.

For each problem you submit, you should cite your sources by including a list of names of other students with whom you discussed the problem. Instructors do not need to be cited.

We also encourage you to come to instructor and staff office hours for help on homework questions. The homework assignments for this class are quite challenging and most students are not able to successfully complete the homework from attending lecture alone. Make sure to use the resources provided on the Resources tab of the course website, actively participate in group work sessions, and plan to attend office hours at least once a week.

You may post homework-related questions on Ed, though your questions (and answers) should be about approaches, not answers. If your question includes some or all of an answer (even if you’re not sure it’s right), you must make your post private so that others cannot see it. We are not able to tell you whether your answer is correct.

Office Hours

To get help on assignments and concepts, course staff will be hosting several office hours per week. All office hours will be held in person. See the Calendar tab of the course website for the most up-to-date schedule.

Exams 🧪

There will be one Midterm Exam and one Final Exam, both held in person.

The Midterm Exam will be worth 20% of your overall course grade. The Final Exam will be worth 30% of your overall course grade and will be cumulative.

To prepare for exams, you should plan to spend a considerable amount of time working through past exam problems. We’re building a bank of old exam problems, with detailed explanations, at

Redemption Policy

The Final Exam will consist of two parts: a “Midterm” section and a “post-Midterm” section. If you do better on the “Midterm” section of the Final Exam than you did on the original Midterm Exam, your score on the “Midterm” section will replace your original Midterm Exam score. This lowers the stakes of the Midterm Exam and gives you two opportunities to demonstrate your understanding of the content from the first half of the course. This also means that you can miss the Midterm Exam for any reason and have the score be replaced by your score on the “Midterm” section of the Final Exam (though we do not recommend this).

You must take the Final Exam to pass the course.

Policies ✏️


Here’s how we will compute your grade.

Component Weight Notes
Homework 40% replace lowest (iff you attempted it) with highest grade
Group work 10% replace lowest (iff you attempted it) with highest grade
Midterm Exam 20%  
Final Exam 30% see redemption policy above

Late Policy, Slip Days, and Drops

Each student has four slip days (two in the summer sessions) to use throughout the quarter. A slip day can be used to extend the deadline of a homework assignment by 24 hours. You can use at most one slip day on any single homework assignment. Slip days can only be used for homework assignments.

Slip days are applied automatically at the end of the quarter, and you don’t need to ask in order to use one. It’s your responsibility to keep track of how many you have left. If you run out of slip days and submit a homework late, it may still be graded so that you’ll see what questions you missed, but the grade will be changed to a zero at the end of the quarter. If you use more than four slip days, we will count the first four late assignments, and any late assignments after that will get zero scores.

Slip days are designed to be a transparent and predictable source of leniency in deadlines. You can use a slip day if you are too busy to complete a homework on its original due date. But slip days are also meant for things like the internet going down at 11:58 PM just as you go to submit your homework.

If you have something going on in your life that is impeding your ability to do your classwork on time, please reach out to us as soon as possible so we can work something out.

Students on the waitlist or who join the class late are expected to keep up with the work and submit assignments by the deadlines.

The stated policies will be strictly enforced out of fairness for all students.

Regrade Requests

You can ask for a regrade on any assignment if you believe that the grader made a mistake. Remember that clarity is a part of your score — if you had the right idea but were unable to clearly communicate it, you may still not deserve full credit. We ask that you please submit your regrade requests within one week (48 hours in summer sessions) of the assignment grade being released; you can submit regrade requests directly on Gradescope.

Incomplete Grades

In the unfortunate circumstance that you become sick, suffer a loss, or otherwise experience a significant setback that is outside of your control, you may be eligible for an Incomplete grade, which allows you to complete the rest of the work at a later time. If you are experiencing challenges due to circumstances outside your control, please contact me ASAP and we can discuss the best course of action. Note that an Incomplete does not allow you to re-do work that has already been completed, only to do work that hasn’t been completed, so it’s best to reach out right away.

Academic Integrity

In this class, we expect that you will work hard, utilize allowed resources to master the course material, and act with integrity. Learning partially remotely presents new challenges for academic integrity, making it more important than ever to act honorably and make sure that the work you are submitting is reflective of your knowledge and abilities.

The UCSD Policy on Integrity of Scholarship and this syllabus list some of the standards by which you are expected to complete your academic work, but your good ethical judgment is also expected. Ignorance of the rules will not excuse you from any violations.

For this class, the following activities, among others, are considered cheating and are not allowed:

  • Sharing written homework solutions with other students, or viewing written homework solutions from another student.
  • Looking or asking for answers to homework problems in other texts or sources, including the internet and Generative AI tools such as ChatGPT and GitHub Copilot.
  • Collaborating on exams, checking answers on exams, or communicating with any other person while taking an exam.
  • Using unauthorized resources on homeworks or exams, including solutions from past iterations of this course, and AI tools such as ChatGPT and GitHub Copilot.

The following activities are examples of things that are allowed in this class:

  • Discussing homework problems with classmates and the instructional staff.
  • Reading about concepts from lecture in outside texts, including the internet, without looking for answers to specific homework questions. If you accidentally find related material in another source, you must cite the source on your homework and write up your answer without consulting the source. To do otherwise is plagiarism.
    • Note that you can ask Generative AI tools, like ChatGPT, to explain ideas from class, but beware, they aren’t always correct!

Remember, Academic Integrity is about doing your part to act with honesty, trust, fairness, respect, responsibility and courage. If you are suspected of dishonest conduct, you will be reported to the Academic Integrity Office. Violations of the academic integrity policy will result in failing the course, and the Dean of your college may place you on academic probation or suspend or dismiss you from UCSD. Academic integrity violations are serious and the risk is not worth it!

A note on letter grades

The following is adapted from CSE 160 at the University of Washington.

Grading for this class is not curved in the sense that the average is set at (say) a B+ and half of the class must receive a grade lower than that. If everyone does well and shows mastery of the material, everyone can receive an A (this would be awesome!). If no one does well (this is unlikely), then everyone can receive a C.

Grading for this class is curved in the sense that we do not have a pre-defined mapping from homework and exam scores to a final GPA. There is no pre-determined score (e.g., 90% of all possible points) that earns an A or a B or a C or any other grade. To determine the final grade, we will ask questions like “Did this student master the material?”. With that said, grades will not be any stricter than the standard grading scale (where an A+ is a 97+, A is 93+, A- is 90+, etc). For instance, the threshold for an “A” will never be higher than 93%.

Try your best not to worry about grades, and we’ll reciprocate by being fair. We’re in this together 😎.

Support 🤝


From the Office for Students with Disabilities (OSD):

OSD works with students with documented disabilities to review documentation and determine reasonable accommodations. Disabilities can occur in these areas: psychological, psychiatric, learning, attention, chronic health, physical, vision, hearing, and acquired brain injuries, and may occur at any time during a student’s college career. We encourage you to contact the OSD as soon as you become aware of a condition that is disabling so that we can work with you.

If you already have accommodations via OSD, please make sure that we receive your Authorization for Accommodation (AFA) letter by the end of Week 2 so that we can make arrangements for accommodations. Share your AFA letter with the instructor and the Data Science OSD Liaison, who can be reached at

Diversity and Inclusion

We are committed to creating an inclusive learning environment in which individual differences are respected and all students feel comfortable. If you have any suggestions as to how we could create a more inclusive setting, please let us know. We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community. Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and able to thrive.

Acknowledgements 🙏

Thanks to other instructors of this course who have made various contributions, including but not limited to Janine Tiefenbruck, Aobo Li, Yian Ma, Gal Mishne, Justin Eldridge, and Suraj Rampure. Thanks also to the many tutors and TAs who have supported this course since its inception!